Understanding how spatial variation is linked to diversity maintenance in natural communities is a pillar of plant community ecology. Theoretically, a variable landscape can maintain diversity via niche partitioning: different species can trade off in performing better or worse depending on the conditions of the patch they are growing in, and as a result, more species can sustainably coexist in a community than if it were spatially heterogeneous. In the hyperdiverse system of native annual plants in Western Australia, fallen logs may be one of the greatest contributors to generating spatial variation that could help maintain species diversity. Considerable anecdotal evidence suggests that fallen logs generate spatial variation, or patchiness, in the environment (Figure 1), and that species or assemblages of plants may respond differently depending on if they are near logs or not. Despite such anecdotal evidence, it is yet unknown if and how fallen logs contribute to maintaining species diversity in the native annual plant communities of the Western Australian wheat belt.

Figure 1: image of annual plant halos around logs
Figure 1: image of annual plant halos around logs

The project will address the following questions:

Q1) Are/how are plant communities in fallen log patches different from patches that are in the open?

Q2) Why are plant communities in fallen log patches different from patches in the open?

Q3) Are/how are plant species performances affected by proximity to fallen logs?

Hypotheses

The null hypothesis, H0, is that annual plants in fallen log patches are not different in diversity, abundance, or composition from open patches.

In addition to the null hypothesis, the following constitute four, non-mutually exclusive hypotheses concerning how fallen logs may introduce spatial variation in the environment. I include corresponding predictions for how plant communities may differ between fallen log patches as compared to open patches.

H1: Log decomposition creates islands of fertility directly around the fallen log.
Prediction 1: Nutrient composition around logs will be higher than in open plots

Prediction 2: Variations in nutrient composition in log vs open environments will correspond to variations in species composition, abundance, and/or richness in these environments.

Prediction 3: All sown plants will perform best in environments where organic logs have been left ‘insitu’. In locations where logs have been removed or replaced with pvc, the legacy of the nutrient island effect will yield higher sown plant performance than when compared to locations where logs have never been. The effect of the nutrient island in locations where logs have been added to open environments should yeild higher plant performance over time. note: performance is measured in terms of germination rate, survival to fruiting, fecundity, and/or biomass.

H2: Fallen logs alter the microclimate directly around them by providing shade.
Prediction 1: Shade and temperature around logs vs in open plots will be different

Prediction 2: Variation in shade and temperature in log vs open environments will correspond to variation in species composition, abundance, and/or richness in these environments.

Prediction 3: All sown plants will perform best in environments where there are organic or pvc logs, no matter if they have been recently moved or not.

H3: Fallen logs trap dispersing seeds as they are blown along the ground.

Prediction 1: Dispersing seeds accumulate around logs, leading to a denser stand of plants in fallen log patches. Plant abundance in fallen log patches will be higher as compared to open patches. Rare plants will be more common in fallen log patches as compared to open patches

Prediction 2: All sown plants will perform the same in all experimental environments

H4: At least some species perform differently according to variation in log vs. open environments and have short dispersal kernels, causing fitness-density covariance

Figure 2: Photo before germination, after a rain. Notice the seeming wet halo under and around the branch
Figure 2: Photo before germination, after a rain. Notice the seeming wet halo under and around the branch


Experimental Design

In this experiment, 224 plots are arranged in 7 blocks of 32 plots each within the Caron Dam nature reserve. A map can be found here.
note: the location info for 3.02 is probably incorrect as of May 2022, and location info is currently unavailable for plots 6.25 and 7.19

Each block is approximately 30m X 30m in area. Plots are 1m long and linear, and have a pin tag on either end (see Figure 3). The pin tags have the identity of the plot written on them in the form of “blocknumber.plotnumber”. Plots are 1m or more away from each other.

In each block, plot environments can be one of six types:
- A 1m log that is out in the open (open_with_log, 4 plots)
- A 1m log that is a part of a tree (insitu_log, 4 plots)
- A 1m pvc pipe that is out in the open (open_with_pvc, 4 plots)
- A 1m pvc pipe that is a part of a tree (insitu_pvc, 4 plots)
- A plot that is out in the open (open, 8 plots)
- A gap in a log where a log used to be (gap, 8 plots)

In half of the plots (not including open plots), the addition, exchange, or removal of logs or pvc to the environment was implemented in October 2020, before seed dispersal. In the other half of these plots, these manipulations were implemented after seed dispersal, in March 2021.

Within each 1m long plot, there is a ~20cm long microtransect. The ends of the microtransects are marked by a nail and a washer sunken into the ground. Each microtransect is approximately 21 cm in internal length from inner washer edge to inner washer edge. Microtransects are not sided.

In half of all plots, seeds were sown in March 2021 and February 2022. In these plots, 15 seeds each of Trachymene ornata (TROR), Goodenia rosea (GORO), and Trachymene cyanopetala (TRCY) are sown outside of the microtransects as in the diagram. These plants were selected because they represent plants common to communities next to logs (TROR), out in the open (GORO), or both (TRCY). The plots where seeds were sown are called ‘lambda’ plots as noted in Figure 3. In the dataset, the rows with a “1” in the ‘seeding_trt’ column are the plots that had seeds sown into them.

Figure 3: plot schematic
Figure 3: plot schematic

Datasets

The sets of data that we have collected for this arm of the project are the following.

  1. Community data, before and after the experiment was implemented.
  1. Soil nutrient analysis in the open and insitu log plots.
  1. Performance data of TROR, TRCY, and TROR.

Methods and analysis

Q1 Are/how are plant communities in fallen log patches different from patches that are in the open?

Overview of results

Quick recap:

  • Plant abundance did not differ between log and open patches every year.
  • Plant diversity is higher in log patches in 2022. When pulling data from 2020-2022 together, plant diversity and likelihood of having positive Shannon diversity index are higher in log patches.
  • Plot type (log vs open) explained a relatively small portion of the variance in plant species composition across plots ranging from 6-12% across years. Visually, the composition of plants tends to consistently differ between plot types in the non-dimensional space.

To compare the characteristic of plant communities in fallen log patches and open patches, we assessed differences in plant abundance, diversity using the Shannon diversity index, and plant composition summarized at the transect level. The analysis is organized by year - we first analysed plot level community data that was compiled from 2020 to 2022. In other words, each row in the data set represents an individual plot from a specific year. After that, we analysed plot level data from 2020, 2021, and 2022 separately. Next, we analysed the plot level data for each year separately: 2020, 2021, and 2022. This resulted in four levels of analysis: 1. 2020-2022 composite data, 2. 2020 data, 3. 2021 data, and 4. 2022 data. For each year, we evaluated three response variables: abundance, diversity, and plant composition.

2020-2022 There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and truncated-Gaussian distributiobns), and composition matrix.

  1. Abundance – the abundance of plants in log patches and open patches that are similar (treatment: p > 0.05). The presented model has a structure of “count ~ treatment(log/open) + (1|block) + (1|year)”.
  2. Diversity – the probability of having a positive Shannon diversity index in the plant community is lower in open patches by 6.04% (treatment: p = 0.03). The plant diversity, as measured by the Shannon diversity index, is higher in log patches by 0.09 units (treatment: p = 0.02). The presented model has a structure of “diversity ~ treatment(log/open) + (1|block) + (1|year)”.
  3. Composition – the five-dimensional NMDS ordination has stress values of 9%, which suggests a fairly good fit for the ordinations. Each two-dimensional NMDS plot can be separated into two groups that overlap - fallen log patches and open patches. In the nondimensional space, the relative position of log plots tend to consistently deviate away from open plots. This can be interpreted as a turnover in species composition at the scale of the plot type. At the same time, relative position of plots sampled from the same block are typically close to each other in nondimensional space, reflecting the similarity of plant communities at a greater spatial level (i.e. block).

Adjusted R2 of the partial RDA is 5.62%. The conditioned terms Block and Year accounted for 44.3% of variations in species composition. Plot type (constrained term) accounted for about 6% of variations in species composition. 49.6% of variation is unexplained (unconstrained).

2020

There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix.

  1. Abundance – the abundance of plants in log patches and open patches that are similar (treatment: p > 0.05). The presented model has a structure of “count ~ treatment(log/open) + (1|block)”.
  2. Diversity – the plant diversity and the probability of having a positive Shannon diversity index in the plant community between log and open patches are similar (treatment: p > 0.05). The presented model has a structure of “diversity ~ treatment(log/open) + (1|block)”.
  3. Composition – the four-dimensional NMDS ordination has stress values of 4.8%, which suggests a very good fit for the ordinations. Similar discussion as in 2020-2022.

Adjusted R2 of the partial RDA is 8.5%. The conditioned term, Block, accounted for 69.6% of variations in species composition. Plot type (constrained term) accounted for about 8.3% of variations in species composition. 22.6% of variation is unexplained (unconstrained).

2021

There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix.

  1. Abundance – the abundance of plants in log patches and open patches that are similar (treatment: p > 0.05). The presented model has a structure of “count ~ treatment(log/open) + (1|block)”.
  2. Diversity – the plant diversity and the probability of having a positive Shannon diversity index in the plant community between log and open patches are similar (treatment: p > 0.05). The presented model has a structure of “diversity ~ treatment(log/open) + (1|block)”.
  3. Composition – The five-dimensional NMDS ordination has stress values of 4.1%, which suggests a very good fit for the ordinations. Similar discussion as in 2020-2022.

Adjusted R2 of the partial RDA is 11.9%. The conditioned term, Block, accounted for 53.7% of variations in species composition. Plot type (constrained term) accounted for about 12.1% of variations in species composition. 34.2% of variation is unexplained (unconstrained).

2022

There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix.

  1. Abundance – the abundance of plants in log patches and open patches that are similar (treatment: p > 0.05). The presented model has a structure of “count ~ treatment(log/open) + (1|block)”.
  2. Diversity – the probability of having a positive Shannon diversity index in the plant community between log and open patches are similar (treatment: p > 0.05). The plant diversity, as measured by the Shannon diversity index, higher in log patches by 0.24 units (treatment: p = 0.007). The presented model has a structure of “diversity ~ treatment(log/open) + (1|block)”.
  3. Composition – The five-dimensional NMDS ordination has stress values of 4%, which suggests a very good fit for the ordinations. Similar discussion as in 2020-2022.

Adjusted R2 of the partial RDA is 11.0%. The conditioned term, Block, accounted for 55.7% of variations in species composition. Plot type (constrained term) accounted for about 11.4% of variations in species composition. 33.0% of variation is unexplained (unconstrained).

Statistical Methods

  1. Abundance comparison

Comparison of plant abundance between log patches and open patches involves two types of data: (1) plant counts of all plots in 2020 before the experiment setup differentiated by the initial treatment “log” and “open”; and (2) plant counts from insitu_log and insitu_open plots in 2021 and 2022 respectively. All count data used for abundance comparison includes species with unknown identity. We used a generalized linear mixed-effects model (GLMER) to model the abundance of plant individuals (2020, 2021 and 2022 data) in unaltered fallen log patches and open patches using a Poisson distribution structure with block as random terms. When pulling count data from 2020 to 2022 together, we included time as a random term to avoid pseudo-replication.

  1. Diversity comparison

A 20-centimeter linear transect was set up for each plot in 2020. We recorded species identity and individual counts for all species occurring along the transects in 2020, 2021 and 2022 respectively. Comparison of plant diversity between log patches and open patches involves two types of data: (1) plant count for each species of all plots in 2020 before the experiment setup differentiated by the initial treatment “log” and “open”; and (2) plant count for each species from insitu_log and insitu_open plots in 2021 and 2022 respectively. All data used for diversity comparison excludes species with unknown identity.

We used the Shannon diversity index to calculate the plant species diversity for each transect at the plot level. A considerable number of zeros are generated in the computation of the Shannon diversity index. We first tried modelling diversity data with a linear mixed-effect model (LMER) assuming a Gaussian distribution. The resulting model was zero-inflated as testing with DHARMa.

As a solution, we used a two-component hurdle model on the truncated data set where the response diversity index was separated into zero and non-zero values. In the first component, we used a binomial GLMER to model the probability of zeros and non-zeros in the diversity index with blocks as random terms. For the second component, we assumed an approximate Gaussian distribution of the non-zero Shannon index and used a linear mixed-effects regression (LMER) to model the response non-zero diversity index with treatment as a fixed effect (log vs open) and block as a random factor. Specifically, for the hurdle model on composite diversity data (2020 to 2022), we also included year as a random factor.

  1. Composition

Comparison of plant composition between log patches and open patches involves two types of data: (1) plant count for each species of all plots in 2020 before the experiment setup differentiated by the initial treatment “log” and “open”; and (2) plant count for each species from insitu_log and insitu_open plots in 2021 and 2022 respectively. All data used for diversity comparison excludes species with unknown identity. For transects where no plants were found, an artificial species, “x,” was added to the species composition matrix to represent zero plants.

We used Non-Metric Dimensional Scaling (NMDS) to analyse the differences in plant communities with Bray-Curtis dissimilarity metrics. We retained two dimensions in the NMDS ordination plot for the visualization and interpretation of plant community composition differences between plot types and blocks.

To identify the contribution of each species to the compositional changes amongst different sample plots, we extracted ordination scores for each species along NMDS axes 1 and 2. The species scores represent the weighted average of a species’ abundance score in a sample community along our selected NMDS axes. We also ran a partial Redundancy Analysis (RDA) to test the correlation between species composition (squared-root transformed) and plot type (log vs open patch) with block as a random term.

Analysis

Abundance analysis
** 2020 - 2022 **
  Abundance:2020-2022 Abundance:2020 Abundance:2021 Abundance:2022
Predictors Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p
(Intercept) 6.79 4.88 – 9.46 <0.001 4.74 4.34 – 5.18 <0.001 7.14 6.20 – 8.22 <0.001 8.82 7.56 – 10.30 <0.001
init [open] 1.00 0.93 – 1.09 0.913 0.94 0.83 – 1.07 0.355 1.09 0.92 – 1.28 0.322 1.04 0.89 – 1.21 0.642
Random Effects
σ2 0.14 0.20 0.12 0.10
τ00 0.00 block 0.00 block 0.00 block 0.01 block
0.08 time      
ICC 0.38 0.00 0.03 0.12
N 7 block 7 block 7 block 7 block
3 time      
Observations 391 221 86 84
Marginal R2 / Conditional R2 0.000 / 0.378 0.004 / 0.009 0.012 / 0.037 0.002 / 0.127
  Abundance:2020-2022 Abundance:2020 Abundance:2021 Abundance:2022
Predictors Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p
(Intercept) 6.79 4.88 – 9.46 <0.001 4.74 4.34 – 5.18 <0.001 7.14 6.20 – 8.22 <0.001 8.82 7.56 – 10.30 <0.001
init [open] 1.00 0.93 – 1.09 0.913 0.94 0.83 – 1.07 0.355 1.09 0.92 – 1.28 0.322 1.04 0.89 – 1.21 0.642
Random Effects
σ2 0.14 0.20 0.12 0.10
τ00 0.00 block 0.00 block 0.00 block 0.01 block
0.08 time      
ICC 0.38 0.00 0.03 0.12
N 7 block 7 block 7 block 7 block
3 time      
Observations 391 221 86 84
Marginal R2 / Conditional R2 0.000 / 0.378 0.004 / 0.009 0.012 / 0.037 0.002 / 0.127

Diversity analysis
## Loading required package: lmerTest
## 
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
## 
##     lmer
## The following object is masked from 'package:stats':
## 
##     step
## Loading required package: performance
## Loading required package: DHARMa
## This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')
## Loading required package: patchwork
## Warning in labdsv::matrify(commsub): NAs introduced by coercion
## `summarise()` has grouped output by 'time', 'block', 'transect', 'init'. You
## can override using the `.groups` argument.
Shannon diversity index (SDI): 2020-2022
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 2.24 0.47 1.32 – 3.17 <0.001 1.08 0.05 0.98 – 1.17 <0.001
init [open] -0.60 0.28 -1.15 – -0.06 0.030 -0.09 0.04 -0.17 – -0.02 0.019
Random Effects
σ2 3.29 0.12
τ00 0.12 block 0.00 block
0.40 time 0.00 time
ICC 0.13 0.04
N 7 block 7 block
3 time 3 time
Observations 393 319
Marginal R2 / Conditional R2 0.023 / 0.155 0.017 / 0.059
Shannon diversity index (SDI): 2020-2022
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 2.24 0.47 1.32 – 3.17 <0.001 1.08 0.05 0.98 – 1.17 <0.001
init [open] -0.60 0.28 -1.15 – -0.06 0.030 -0.09 0.04 -0.17 – -0.02 0.019
Random Effects
σ2 3.29 0.12
τ00 0.12 block 0.00 block
0.40 time 0.00 time
ICC 0.13 0.04
N 7 block 7 block
3 time 3 time
Observations 393 319
Marginal R2 / Conditional R2 0.023 / 0.155 0.017 / 0.059
Shannon diversity index (SDI): 2020
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 1.25 0.25 0.76 – 1.74 <0.001 1.00 0.04 0.93 – 1.08 <0.001
init [open] -0.37 0.31 -0.97 – 0.24 0.235 -0.08 0.05 -0.19 – 0.02 0.132
Random Effects
σ2 3.29  
τ00 0.06 block  
ICC 0.02  
N 7 block  
Observations 223 165
Marginal R2 / Conditional R2 0.010 / 0.029 0.014 / 0.008
Shannon diversity index (SDI): 2020
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 1.25 0.25 0.76 – 1.74 <0.001 1.00 0.04 0.93 – 1.08 <0.001
init [open] -0.37 0.31 -0.97 – 0.24 0.235 -0.08 0.05 -0.19 – 0.02 0.132
Random Effects
σ2 3.29  
τ00 0.06 block  
ICC 0.02  
N 7 block  
Observations 223 165
Marginal R2 / Conditional R2 0.010 / 0.029 0.014 / 0.008
Shannon diversity index (SDI): 2021
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 3.37 1.02 1.83 – 6.25 0.001 1.06 0.06 0.93 – 1.19 <0.001
init [open] -1.42 1.09 -4.38 – 0.37 0.194 0.00 0.08 -0.16 – 0.16 0.981
Random Effects
σ2   0.11
τ00   0.00 block
ICC   0.02
N   7 block
Observations 86 78
R2 Tjur 0.023 0.000 / 0.017
Shannon diversity index (SDI): 2021
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 3.37 1.02 1.83 – 6.25 0.001 1.06 0.06 0.93 – 1.19 <0.001
init [open] -1.42 1.09 -4.38 – 0.37 0.194 0.00 0.08 -0.16 – 0.16 0.981
Random Effects
σ2   0.11
τ00   0.00 block
ICC   0.02
N   7 block
Observations 86 78
R2 Tjur 0.023 0.000 / 0.017 
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
Shannon diversity index (SDI): 2022
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 29.91 915.89 -1765.21 – 1825.03 0.974 1.21 0.08 1.06 – 1.36 <0.001
init [open] -27.74 915.89 -1822.85 – 1767.38 0.976 -0.24 0.09 -0.41 – -0.07 0.007
Random Effects
σ2 3.29 0.13
τ00 1.15 block 0.01 block
ICC 0.26 0.07
N 7 block 7 block
Observations 84 76
Marginal R2 / Conditional R2 0.975 / 0.981 0.088 / 0.149
Shannon diversity index (SDI): 2022
  Prob. SDI>0 Pron. when SDI>0
Predictors Log-Odds std. Error CI p Estimates std. Error CI p
(Intercept) 29.91 915.89 -1765.21 – 1825.03 0.974 1.21 0.08 1.06 – 1.36 <0.001
init [open] -27.74 915.89 -1822.85 – 1767.38 0.976 -0.24 0.09 -0.41 – -0.07 0.007
Random Effects
σ2 3.29 0.13
τ00 1.15 block 0.01 block
ICC 0.26 0.07
N 7 block 7 block
Observations 84 76
Marginal R2 / Conditional R2 0.975 / 0.981 0.088 / 0.149

Composition analysis

Composition dissimilarity * 2020 *

## 
## Call:
## rda(X = ass.rel.t0, Y = init, Z = block) 
## 
## Partitioning of variance:
##               Inertia Proportion
## Total         0.42624    1.00000
## Conditioned   0.29429    0.69045
## Constrained   0.03551    0.08332
## Unconstrained 0.09643    0.22624
## 
## Eigenvalues, and their contribution to the variance 
## after removing the contribution of conditiniong variables
## 
## Importance of components:
##                          RDA1     PC1     PC2     PC3     PC4     PC5      PC6
## Eigenvalue            0.03551 0.03207 0.02047 0.01524 0.01394 0.01034 0.004375
## Proportion Explained  0.26916 0.24303 0.15511 0.11552 0.10566 0.07837 0.033159
## Cumulative Proportion 0.26916 0.51219 0.66730 0.78282 0.88848 0.96684 1.000000
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                          RDA1
## Eigenvalue            0.03551
## Proportion Explained  1.00000
## Cumulative Proportion 1.00000
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  1.534259 
## 
## 
## Species scores
## 
##             RDA1        PC1        PC2        PC3        PC4        PC5
## acul   0.1067309 -9.564e-02 -2.516e-02  6.791e-02 -5.735e-02 -1.985e-02
## aicu   0.0342552  7.579e-02 -2.904e-02 -1.040e-01 -1.656e-02  3.629e-04
## arca   0.0000000 -5.092e-17  4.573e-17 -2.095e-17  2.733e-18  2.352e-18
## ardy   0.0000000 -1.340e-17  2.878e-18  4.804e-18  6.089e-18  3.913e-18
## arsp   0.0212814 -9.296e-03  3.074e-02 -1.243e-02  2.902e-02  2.380e-02
## auel   0.0000000  1.657e-17  1.767e-18 -7.370e-18 -1.235e-17 -4.376e-19
## bldr   0.0627812 -3.489e-02  5.885e-02 -3.487e-02  5.598e-02 -1.193e-01
## blrd   0.0000000  2.296e-18  1.300e-17  5.184e-19  1.453e-17  5.429e-18
## brdi   0.0000000 -1.645e-17  1.224e-17 -1.804e-17 -5.031e-18  1.778e-17
## brdr   0.0000000 -1.535e-18  1.057e-18  1.817e-19  3.219e-18  1.290e-18
## brpe   0.0000000  4.493e-17  7.212e-18 -2.652e-17 -7.924e-17 -1.269e-17
## brru   0.0000000 -3.497e-33 -2.348e-33  3.058e-33  5.468e-33 -1.861e-34
## buse  -0.0399181  8.064e-03 -8.920e-03 -7.638e-03 -1.217e-02  6.087e-02
## caer  -0.0770469  5.739e-03 -4.007e-02  2.232e-02 -3.681e-02  4.977e-02
## cagr  -0.0353354  3.492e-02 -5.082e-02 -7.084e-02  2.218e-02  4.523e-02
## cahi   0.0073560  1.491e-02  4.828e-02 -6.274e-03  3.863e-02  1.972e-02
## casp   0.0071091 -7.320e-02 -4.968e-02  8.494e-03  2.080e-02  8.123e-03
## cear   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chau   0.0270050  2.642e-02  5.365e-02 -7.004e-02 -9.242e-02 -2.682e-03
## chei   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chps   0.0867979 -8.706e-02 -4.807e-02  5.256e-03 -3.053e-02 -5.813e-02
## crcl   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## crco  -0.0054567 -2.817e-02 -2.789e-02 -6.578e-02 -6.492e-03  6.700e-02
## cusc   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## cusp   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## dagl  -0.0005158 -4.170e-02 -1.197e-02  1.578e-02 -3.486e-02  3.748e-02
## dosp   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ento   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## erau   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ercy   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## erra  -0.0017634  1.363e-03 -1.961e-03 -2.641e-03  6.378e-04  2.340e-03
## ersp  -0.0209707  1.621e-02 -2.332e-02 -3.140e-02  7.584e-03  2.783e-02
## gite  -0.0057450 -7.634e-03  5.428e-03 -7.119e-03 -3.080e-03 -1.608e-03
## gnte   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gobe  -0.0320039 -3.133e-03 -9.076e-02  5.077e-02  1.243e-01  2.247e-02
## gocy  -0.0012808  6.463e-02  4.761e-02 -1.112e-02 -1.947e-02 -4.368e-03
## gono   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## goro   0.0215820  2.211e-01 -1.082e-01 -1.141e-02 -4.094e-02 -1.836e-02
## gosp   0.0446889  2.138e-02  1.224e-02  1.185e-02  3.669e-02  2.988e-02
## haod  -0.0050284 -3.182e-02  7.735e-03  1.165e-02  6.116e-02  1.742e-02
## hygl   0.0601486  6.535e-02  7.287e-02  6.640e-02 -5.544e-02  7.196e-02
## hypi   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## hypo   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## jubu   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## laro  -0.1146604 -4.765e-02 -1.202e-02  3.910e-02 -4.021e-02 -1.545e-02
## ledu   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## lele   0.0212814 -9.296e-03  3.074e-02 -1.243e-02  2.902e-02  2.380e-02
## loef   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## misp  -0.0859180  4.494e-02 -1.140e-01 -3.971e-02 -2.276e-02  5.166e-03
## mite   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## momo   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mopa   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## niro   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## omco   0.0102406 -1.780e-02 -1.290e-02 -4.528e-03 -7.063e-03  3.001e-03
## orsp   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pala   0.0212814 -9.296e-03  3.074e-02 -1.243e-02  2.902e-02  2.380e-02
## peai   0.0697049  5.909e-02 -2.821e-03  5.278e-02 -3.971e-02 -2.331e-02
## pedu   0.0300964 -1.315e-02  4.347e-02 -1.758e-02  4.104e-02  3.366e-02
## phsu   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## plde   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poar   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poca   0.0311535  7.429e-02  1.806e-02  8.779e-02  2.715e-02 -2.008e-02
## pocap  0.0157620  1.774e-02  1.191e-02 -1.551e-02 -2.774e-02  9.443e-04
## poce   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pogn   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pole  -0.0067798  6.543e-02  2.974e-02  8.404e-02 -4.950e-03 -2.006e-02
## pomu   0.1806915 -8.052e-02 -1.066e-01  1.495e-02 -1.385e-02 -5.198e-02
## pter   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ptga  -0.0265218  3.413e-02  8.198e-05  2.118e-02  4.241e-02  3.353e-02
## ptob   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## rhla   0.0114587  1.612e-02 -1.907e-02  1.253e-01 -6.133e-02  3.825e-02
## rhpy  -0.0614885  1.909e-01  1.451e-02  3.551e-03  8.057e-02 -4.550e-02
## rhsp   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ry     0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## scna   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## sino   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## sool   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## stfi  -0.0217745 -2.451e-02 -1.645e-02  2.143e-02  3.832e-02 -1.304e-03
## stpi   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## thma   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## trcy  -0.2016923  4.678e-02  1.599e-01 -2.560e-02 -4.775e-02 -1.767e-02
## tris   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## tror  -0.1560795 -5.629e-02 -1.247e-02 -3.574e-02 -4.124e-02 -1.417e-02
## trpi  -0.0192367 -2.197e-03  9.150e-03 -9.411e-03  2.883e-03 -2.219e-02
## waac  -0.1168716 -1.021e-01  4.820e-02 -2.229e-02  7.287e-04 -5.756e-03
## wagr  -0.0763637  1.108e-02 -5.682e-02 -5.042e-02 -3.786e-02  2.435e-03
## x     -0.0563652 -6.808e-02  4.036e-02 -1.343e-02  3.574e-02  6.019e-02
## 
## 
## Site scores (weighted sums of species scores)
## 
##          RDA1      PC1     PC2     PC3      PC4      PC5
## sit1  -0.4306 -0.15560  0.2593  0.4984 -0.43518  0.64698
## sit2   0.4306  0.15560 -0.2593 -0.4984  0.43518 -0.64698
## sit3  -0.5088 -0.54489  0.3874 -0.5081 -0.21987 -0.11475
## sit4   0.5088  0.54489 -0.3874  0.5081  0.21987  0.11475
## sit5  -0.3922 -0.04684  0.1950 -0.2006  0.06145 -0.47310
## sit6   0.3922  0.04684 -0.1950  0.2006 -0.06145  0.47310
## sit7  -0.5074  0.31689 -0.4561 -0.6141  0.14830  0.54414
## sit8   0.5074 -0.31689  0.4561  0.6141 -0.14830 -0.54414
## sit9  -0.5125  0.71285  0.5165  0.1813  0.28281 -0.12016
## sit10  0.5125 -0.71285 -0.5165 -0.1813 -0.28281  0.12016
## sit11 -0.2572  0.17912 -0.5923  0.2395 -0.55918 -0.45854
## sit12  0.2572 -0.17912  0.5923 -0.2395  0.55918  0.45854
## sit13 -0.2616 -0.46153 -0.3099  0.4036  0.72167 -0.02456
## sit14  0.2616  0.46153  0.3099 -0.4036 -0.72167  0.02456
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##        RDA1      PC1     PC2     PC3      PC4      PC5
## con1  -0.41 -0.15560  0.2593  0.4984 -0.43518  0.64698
## con2   0.41  0.15560 -0.2593 -0.4984  0.43518 -0.64698
## con3  -0.41 -0.54489  0.3874 -0.5081 -0.21987 -0.11475
## con4   0.41  0.54489 -0.3874  0.5081  0.21987  0.11475
## con5  -0.41 -0.04684  0.1950 -0.2006  0.06145 -0.47310
## con6   0.41  0.04684 -0.1950  0.2006 -0.06145  0.47310
## con7  -0.41  0.31689 -0.4561 -0.6141  0.14830  0.54414
## con8   0.41 -0.31689  0.4561  0.6141 -0.14830 -0.54414
## con9  -0.41  0.71285  0.5165  0.1813  0.28281 -0.12016
## con10  0.41 -0.71285 -0.5165 -0.1813 -0.28281  0.12016
## con11 -0.41  0.17912 -0.5923  0.2395 -0.55918 -0.45854
## con12  0.41 -0.17912  0.5923 -0.2395  0.55918  0.45854
## con13 -0.41 -0.46153 -0.3099  0.4036  0.72167 -0.02456
## con14  0.41  0.46153  0.3099 -0.4036 -0.72167  0.02456
## 
## 
## Biplot scores for constraining variables
## 
##       RDA1 PC1 PC2 PC3 PC4 PC5
## Yopen    1   0   0   0   0   0
## [1] 0.08471055
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: rda(X = ass.rel.t0, Y = init, Z = block)
##          Df Variance      F Pr(>F)  
## Model     1 0.035514 2.2097  0.047 *
## Residual  6 0.096430                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Composition dissimilarity * 2021 *

## 
## Call:
## rda(X = ass.rel.t1, Y = init, Z = block) 
## 
## Partitioning of variance:
##               Inertia Proportion
## Total         0.53262     1.0000
## Conditioned   0.28621     0.5373
## Constrained   0.06436     0.1208
## Unconstrained 0.18206     0.3418
## 
## Eigenvalues, and their contribution to the variance 
## after removing the contribution of conditiniong variables
## 
## Importance of components:
##                          RDA1     PC1     PC2     PC3     PC4     PC5     PC6
## Eigenvalue            0.06436 0.05497 0.03785 0.02947 0.02799 0.02046 0.01132
## Proportion Explained  0.26118 0.22307 0.15362 0.11959 0.11357 0.08304 0.04593
## Cumulative Proportion 0.26118 0.48425 0.63787 0.75745 0.87103 0.95407 1.00000
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                          RDA1
## Eigenvalue            0.06436
## Proportion Explained  1.00000
## Cumulative Proportion 1.00000
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  1.62215 
## 
## 
## Species scores
## 
##            RDA1        PC1        PC2        PC3        PC4        PC5
## acul   0.055571  3.637e-02  4.328e-02  1.783e-02  5.246e-03  5.454e-04
## aicu   0.000000  8.472e-17 -2.496e-17 -1.813e-17  2.580e-17 -2.616e-17
## arca   0.000000  9.597e-18  2.861e-18  1.596e-17 -2.682e-18 -9.970e-19
## ardy   0.042259  5.025e-02  5.860e-02  1.652e-02  4.875e-02  2.875e-02
## arsp   0.000000  1.984e-17  3.886e-17  5.839e-17 -3.819e-17  1.599e-17
## auel   0.000000  9.860e-19 -4.413e-18  8.306e-18 -3.824e-18 -6.153e-18
## bldr  -0.039796 -4.819e-03  3.972e-02 -4.794e-02 -2.337e-02  2.293e-02
## blrd   0.042899  4.726e-03  1.765e-02  2.217e-02 -5.565e-02 -2.605e-02
## brdi   0.000000 -1.170e-17 -1.637e-17 -2.608e-17 -3.440e-18 -2.426e-17
## brdr   0.000000 -2.443e-19  4.135e-18 -1.727e-18 -3.647e-18  3.050e-18
## brpe  -0.128276 -8.636e-02 -6.995e-02 -9.422e-02 -6.120e-02 -1.120e-02
## brru   0.000000 -1.871e-34  5.118e-33 -4.301e-33 -3.629e-33  2.180e-33
## buse   0.000000  5.868e-34 -1.605e-32  1.349e-32  1.138e-32 -6.838e-33
## caer   0.011258 -7.733e-02  7.189e-02  4.870e-03  3.834e-02 -9.362e-02
## cagr  -0.095632 -8.997e-02 -1.221e-02  1.902e-02 -2.621e-02 -1.103e-01
## cahi   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## casp  -0.003109  1.770e-02 -2.479e-02 -2.422e-02  2.065e-02  6.085e-02
## cear   0.042899  2.468e-03 -6.751e-02  5.673e-02  4.787e-02 -2.876e-02
## chau   0.140877 -1.835e-01 -1.649e-02  1.254e-02  8.452e-02  1.746e-02
## chei   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chps   0.213452  9.333e-02  4.706e-02 -1.968e-02 -1.078e-01  1.722e-02
## crcl   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## crco   0.138108  1.593e-01 -8.706e-02  4.536e-02 -1.939e-02  3.021e-02
## cusc   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## cusp   0.023897  4.130e-03  2.468e-02  9.589e-02 -9.788e-02  9.619e-03
## dagl   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## dosp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ento   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## erau   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ercy  -0.129183 -3.188e-02  1.257e-02  8.821e-02  5.864e-02  3.511e-02
## erra   0.021450 -2.405e-03 -1.984e-03  4.725e-03 -3.054e-02 -2.041e-02
## ersp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gite  -0.059503  1.330e-01 -2.802e-02 -9.877e-04 -2.136e-02 -4.809e-02
## gnte   0.105088 -1.192e-01 -1.940e-02  3.043e-03  6.651e-02 -1.381e-03
## gobe   0.009616  2.117e-02  6.289e-02 -1.833e-01 -6.378e-02  4.242e-02
## gocy   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gono   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## goro   0.087805  8.971e-02  6.801e-02 -8.319e-02  1.119e-01 -7.619e-02
## gosp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## haod  -0.032312 -2.971e-03 -4.026e-03  6.188e-02 -2.109e-02  4.142e-02
## hygl   0.215609  4.905e-02  1.609e-03  6.009e-02  1.333e-02  5.224e-02
## hypi  -0.035126 -4.177e-02 -4.871e-02 -1.374e-02 -4.052e-02 -2.390e-02
## hypo   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## jubu   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## laro  -0.062906 -4.731e-03  4.412e-02  2.142e-02 -5.523e-02  6.194e-02
## ledu   0.004284  1.424e-03  3.922e-03  3.485e-03 -5.014e-03 -1.127e-03
## lele   0.019834  1.824e-03  2.471e-03 -3.798e-02  1.295e-02 -2.543e-02
## loef   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## misp  -0.077168 -1.527e-01  1.772e-02 -2.016e-02 -2.886e-02 -4.694e-02
## mite   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## momo   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mopa   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## niro  -0.036841 -1.225e-02 -3.373e-02 -2.997e-02  4.312e-02  9.689e-03
## omco   0.022017  1.488e-02 -2.711e-02 -1.869e-02 -1.512e-02  3.695e-02
## orsp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pala   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## peai   0.031786 -6.717e-02 -6.703e-02 -9.866e-02  1.006e-01  1.083e-01
## pedu   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## phsu  -0.026050 -8.660e-03 -2.385e-02 -2.119e-02  3.049e-02  6.851e-03
## plde   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poar   0.006522 -1.086e-01  5.808e-02  2.807e-02  3.614e-02 -2.284e-02
## poca   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pocap  0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poce   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pogn   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pole  -0.037141 -1.016e-01 -9.293e-02 -3.438e-02 -7.013e-02 -1.911e-02
## pomu   0.047780  9.595e-02  1.336e-01  5.875e-02  9.945e-02  8.749e-02
## pter   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ptga   0.126430 -1.396e-01  1.126e-03 -9.388e-03 -4.387e-02  5.596e-02
## ptob   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## rhla   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## rhpy  -0.080545 -6.671e-02 -9.829e-02  1.253e-01 -1.163e-01  2.039e-02
## rhsp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## ry     0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## scna   0.043870 -4.887e-02 -2.320e-02  2.874e-02  3.198e-02  3.740e-03
## sino   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## sool  -0.032312 -2.971e-03 -4.026e-03  6.188e-02 -2.109e-02  4.142e-02
## stfi   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## stpi   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## thma   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## trcy  -0.192212  8.297e-02 -2.570e-01 -7.513e-02  4.924e-02  3.987e-03
## tris   0.061108 -4.018e-02 -3.765e-02  9.554e-02  4.144e-02  1.226e-02
## tror  -0.092188 -2.616e-02  1.417e-01 -5.750e-02 -5.785e-02 -1.555e-02
## trpi  -0.084150  1.881e-01 -3.963e-02 -1.397e-03 -3.021e-02 -6.801e-02
## waac  -0.075766  3.898e-02 -3.149e-02  5.714e-02  8.339e-02 -1.179e-01
## wagr   0.056986  1.417e-02  3.648e-02 -7.759e-03 -3.054e-02 -3.520e-02
## x      0.022017  1.488e-02 -2.711e-02 -1.869e-02 -1.512e-02  3.695e-02
## 
## 
## Site scores (weighted sums of species scores)
## 
##          RDA1      PC1      PC2       PC3     PC4     PC5
## sit1  -0.3912 -0.29299  0.53388  0.367996  0.2978 -0.7276
## sit2   0.3912  0.29299 -0.53388 -0.367996 -0.2978  0.7276
## sit3  -0.4999  0.96887 -0.20417 -0.007197 -0.1556 -0.3504
## sit4   0.4999 -0.96887  0.20417  0.007197  0.1556  0.3504
## sit5  -0.5201 -0.14413 -0.39689 -0.352694  0.5075  0.1140
## sit6   0.5201  0.14413  0.39689  0.352694 -0.5075 -0.1140
## sit7  -0.2983  0.04860  0.04011 -0.095492  0.6173  0.4125
## sit8   0.2983 -0.04860 -0.04011  0.095492 -0.6173 -0.4125
## sit9  -0.6722 -0.51555 -0.60119 -0.169521 -0.5001 -0.2950
## sit10  0.6722  0.51555  0.60119  0.169521  0.5001  0.2950
## sit11 -0.3244 -0.03987 -0.05401  0.830224 -0.2830  0.5558
## sit12  0.3244  0.03987  0.05401 -0.830224  0.2830 -0.5558
## sit13 -0.3287 -0.02494  0.68227 -0.573317 -0.4838  0.2906
## sit14  0.3287  0.02494 -0.68227  0.573317  0.4838 -0.2906
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##          RDA1      PC1      PC2       PC3     PC4     PC5
## con1  -0.4335 -0.29299  0.53388  0.367996  0.2978 -0.7276
## con2   0.4335  0.29299 -0.53388 -0.367996 -0.2978  0.7276
## con3  -0.4335  0.96887 -0.20417 -0.007197 -0.1556 -0.3504
## con4   0.4335 -0.96887  0.20417  0.007197  0.1556  0.3504
## con5  -0.4335 -0.14413 -0.39689 -0.352694  0.5075  0.1140
## con6   0.4335  0.14413  0.39689  0.352694 -0.5075 -0.1140
## con7  -0.4335  0.04860  0.04011 -0.095492  0.6173  0.4125
## con8   0.4335 -0.04860 -0.04011  0.095492 -0.6173 -0.4125
## con9  -0.4335 -0.51555 -0.60119 -0.169521 -0.5001 -0.2950
## con10  0.4335  0.51555  0.60119  0.169521  0.5001  0.2950
## con11 -0.4335 -0.03987 -0.05401  0.830224 -0.2830  0.5558
## con12  0.4335  0.03987  0.05401 -0.830224  0.2830 -0.5558
## con13 -0.4335 -0.02494  0.68227 -0.573317 -0.4838  0.2906
## con14  0.4335  0.02494 -0.68227  0.573317  0.4838 -0.2906
## 
## 
## Biplot scores for constraining variables
## 
##       RDA1 PC1 PC2 PC3 PC4 PC5
## Yopen    1   0   0   0   0   0
## [1] 0.1186067
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: rda(X = ass.rel.t1, Y = init, Z = block)
##          Df Variance     F Pr(>F)  
## Model     1 0.064359 2.121  0.048 *
## Residual  6 0.182059               
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Composition dissimilarity * 2022 *

## 
## Call:
## rda(formula = ass.rel.t2 ~ init + block) 
## 
## Partitioning of variance:
##               Inertia Proportion
## Total          0.5340     1.0000
## Constrained    0.3581     0.6705
## Unconstrained  0.1759     0.3295
## 
## Eigenvalues, and their contribution to the variance 
## 
## Importance of components:
##                         RDA1    RDA2    RDA3    RDA4    RDA5    RDA6    RDA7
## Eigenvalue            0.1078 0.07881 0.05379 0.04086 0.03384 0.02612 0.01683
## Proportion Explained  0.2019 0.14758 0.10073 0.07651 0.06338 0.04891 0.03151
## Cumulative Proportion 0.2019 0.34951 0.45024 0.52675 0.59013 0.63904 0.67055
##                          PC1     PC2     PC3     PC4     PC5     PC6
## Eigenvalue            0.0547 0.03572 0.03141 0.02309 0.01636 0.01464
## Proportion Explained  0.1024 0.06689 0.05882 0.04325 0.03064 0.02742
## Cumulative Proportion 0.7730 0.83988 0.89870 0.94194 0.97258 1.00000
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                         RDA1    RDA2    RDA3    RDA4    RDA5    RDA6    RDA7
## Eigenvalue            0.1078 0.07881 0.05379 0.04086 0.03384 0.02612 0.01683
## Proportion Explained  0.3011 0.22009 0.15022 0.11410 0.09452 0.07293 0.04699
## Cumulative Proportion 0.3011 0.52124 0.67146 0.78556 0.88007 0.95301 1.00000
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  1.623197 
## 
## 
## Species scores
## 
##             RDA1       RDA2       RDA3       RDA4       RDA5       RDA6
## acul   9.052e-02 -6.166e-02 -2.465e-02  1.043e-01  1.350e-01  9.105e-02
## aicu   4.768e-17  4.052e-17 -9.248e-17 -1.056e-17 -1.453e-17 -3.522e-18
## arca  -5.475e-17 -2.441e-18 -4.889e-17  1.376e-17 -1.781e-17  5.141e-17
## ardy  -4.584e-02 -4.434e-02 -1.111e-02  1.969e-02 -2.105e-02 -1.788e-02
## arsp  -1.390e-17  6.109e-19 -1.670e-17  5.467e-17  3.549e-17  3.880e-17
## auel  -1.762e-17  3.587e-17  1.566e-17 -1.261e-17 -4.081e-17 -4.764e-17
## bldr   1.039e-01  4.551e-02 -1.034e-02  5.410e-02  3.353e-02 -5.643e-02
## blrd   1.876e-17 -2.147e-17  1.438e-18  1.943e-17  2.607e-17  1.106e-17
## brdi   1.852e-02  3.590e-02  8.983e-03  2.044e-02  1.514e-03 -4.940e-02
## brdr   6.717e-18 -2.173e-18  1.097e-18  1.249e-18  4.221e-18 -1.502e-17
## brpe   1.354e-17  2.728e-17  4.933e-18  1.483e-17  1.682e-19 -3.402e-17
## brru  -7.711e-33  1.305e-32  2.283e-33  3.454e-33 -6.606e-33  1.260e-32
## buse   2.953e-33 -1.069e-32  2.015e-33 -2.537e-33  5.339e-33 -8.503e-33
## caer   1.272e-01 -6.922e-02  1.166e-01  7.445e-02  2.762e-02  4.229e-03
## cagr   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## cahi   2.197e-02 -8.344e-02  7.037e-02  5.156e-02 -1.853e-02 -2.675e-02
## casp   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## cear   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chau   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chei  -6.027e-02  4.108e-02 -1.022e-01  5.789e-02 -9.473e-02  3.468e-02
## chps   1.422e-01  5.056e-02 -9.443e-02 -2.245e-02  2.895e-02 -2.191e-02
## crcl   7.114e-02 -4.012e-02  4.716e-02 -3.862e-02 -7.741e-02  8.715e-03
## crco   9.734e-02 -6.815e-02 -1.138e-01  5.228e-02  9.551e-02 -5.560e-03
## cusc   9.947e-02 -4.263e-02  1.155e-01 -3.845e-02 -1.031e-01 -3.646e-02
## cusp   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## dagl   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## dosp  -1.315e-01 -1.124e-01 -1.082e-01  5.196e-02 -4.110e-02 -7.973e-02
## ento   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## erau   3.620e-02 -3.512e-02  4.765e-02 -2.634e-02 -4.680e-02  5.787e-03
## ercy   4.416e-02 -5.421e-03 -7.098e-02 -1.592e-01  9.063e-02  1.565e-02
## erra   2.116e-02  3.187e-02 -5.398e-02 -1.259e-02  2.466e-02 -1.379e-02
## ersp   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gite  -2.639e-02 -2.973e-02 -6.122e-02  2.699e-02 -3.764e-02 -2.222e-02
## gnte   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gobe  -1.559e-01  1.913e-01  1.457e-01 -2.624e-03 -2.945e-02  7.403e-02
## gocy   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gono   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## goro  -8.288e-03  1.152e-01 -1.390e-01  5.549e-02 -1.156e-01  3.310e-02
## gosp   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## haod  -9.922e-04  1.618e-01 -1.355e-02 -1.014e-02  4.472e-02 -7.729e-02
## hygl   6.118e-02  9.387e-02  4.443e-02 -1.767e-02 -1.196e-02 -1.030e-01
## hypi   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## hypo  -1.494e-01 -1.129e-01  6.094e-02 -1.241e-01 -4.803e-02  2.488e-02
## jubu   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## laro  -5.349e-02 -2.490e-02  4.872e-02  4.252e-02 -7.231e-03 -7.957e-02
## ledu   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## lele   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## loef  -2.155e-02 -2.427e-02 -4.998e-02  2.204e-02 -3.073e-02 -1.814e-02
## misp   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mite  -1.261e-01  1.544e-01  1.114e-01 -9.659e-03  1.890e-01  3.377e-02
## momo   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mopa   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## niro   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## omco   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## orsp  -5.409e-03  1.374e-02  4.231e-02  1.614e-02  9.775e-03 -4.362e-02
## pala   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## peai  -1.076e-01  3.483e-01 -1.470e-01 -5.028e-02 -1.466e-02 -3.413e-02
## pedu   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## phsu   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## plde   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poar   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poca  -7.330e-02  1.228e-02 -1.627e-02 -1.762e-01  1.119e-01 -9.665e-02
## pocap -9.137e-02  1.421e-02 -2.497e-02  4.681e-02 -6.872e-02  3.860e-02
## poce  -5.409e-03  1.374e-02  4.231e-02  1.614e-02  9.775e-03 -4.362e-02
## pogn   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pole   5.618e-02 -1.627e-01 -9.388e-02 -1.303e-01  5.130e-02  8.606e-03
## pomu   4.919e-01  5.095e-02  2.181e-03 -4.628e-02 -5.128e-02 -4.981e-02
## pter   8.015e-02  4.723e-02 -2.287e-02 -4.203e-03 -2.753e-02 -5.322e-02
## ptga   1.310e-02  2.539e-02  6.352e-03  1.445e-02  1.071e-03 -3.493e-02
## ptob   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## rhla   2.267e-02 -6.908e-02  4.303e-02  6.541e-02  8.281e-02  6.017e-02
## rhpy   1.001e-03  5.611e-02  1.010e-01  5.055e-02  2.293e-02 -1.325e-01
## rhsp  -2.148e-01 -1.818e-01 -9.260e-02 -9.215e-02 -2.889e-02 -6.196e-02
## ry     0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## scna   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## sino   3.620e-02 -3.512e-02  4.765e-02 -2.634e-02 -4.680e-02  5.787e-03
## sool   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## stfi  -5.409e-03  1.374e-02  4.231e-02  1.614e-02  9.775e-03 -4.362e-02
## stpi   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## thma   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## trcy  -1.561e-01 -1.095e-01  2.729e-02  1.591e-01  4.816e-02 -1.420e-01
## tris   4.228e-02 -1.673e-02 -2.024e-02  4.110e-02  3.757e-02  3.527e-02
## tror  -5.230e-02  8.443e-02  1.375e-01  8.368e-03 -4.656e-02  3.086e-02
## trpi   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## waac  -1.822e-02 -3.727e-02  1.688e-01 -1.391e-01 -3.077e-02 -1.163e-02
## wagr   0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## x      0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## 
## 
## Site scores (weighted sums of species scores)
## 
##            RDA1     RDA2    RDA3     RDA4     RDA5     RDA6
## row1  -0.529954 -0.50054  0.2468 -1.10353  0.14120  0.29241
## row2   0.027932  0.29494 -0.3213 -0.55623  0.49222 -0.18686
## row3  -0.526506 -0.90064 -0.2668  0.49320 -0.41943 -0.57329
## row4  -0.521497 -0.16611 -0.6799  0.15480 -0.38435  0.01384
## row5   0.523959 -0.45922  0.7665 -0.33398 -1.07139  0.06484
## row6   0.751980 -0.09337 -0.1892 -0.28283 -0.23276  0.07611
## row7  -0.131864  0.32707 -0.1624 -0.14669  0.60202 -0.12836
## row8   0.300694  0.18578 -0.6404 -0.21465  0.18647 -0.22795
## row9  -0.083385 -0.56645  0.3037  0.69285  1.18053  0.55202
## row10  0.816213 -0.18975 -0.2365  0.35601 -0.04331  0.37932
## row11  0.004328  0.36280  0.7414  0.20897  0.15651 -0.81764
## row12  0.187756  0.43299  0.1371  0.38949  0.03776 -0.70925
## row13 -0.590590  0.63290  0.4722  0.06967 -0.07356  0.61728
## row14 -0.229066  0.63960 -0.1712  0.27293 -0.57192  0.64753
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##           RDA1     RDA2     RDA3    RDA4     RDA5     RDA6
## row1  -0.44158 -0.26053  0.26294 -0.8470  0.39041  0.05381
## row2  -0.06044  0.05493 -0.33743 -0.8128  0.24301  0.05174
## row3  -0.71457 -0.69110 -0.17315  0.3069 -0.32819 -0.27869
## row4  -0.33343 -0.37564 -0.77353  0.3411 -0.47559 -0.28076
## row5   0.44740 -0.43402  0.58885 -0.3255 -0.57837  0.07151
## row6   0.82854 -0.11857 -0.01153 -0.2913 -0.72577  0.06944
## row7  -0.10616  0.09870 -0.10123 -0.1977  0.46794 -0.17712
## row8   0.27499  0.41415 -0.70160 -0.1636  0.32055 -0.17919
## row9   0.17584 -0.53583  0.33379  0.5074  0.64231  0.46671
## row10  0.55699 -0.22037 -0.26659  0.5415  0.49491  0.46464
## row11 -0.09453  0.24017  0.73940  0.2822  0.17083 -0.76241
## row12  0.28661  0.55562  0.13902  0.3163  0.02343 -0.76448
## row13 -0.60040  0.47852  0.45072  0.1542 -0.24904  0.63344
## row14 -0.21925  0.79398 -0.14966  0.1884 -0.39644  0.63137
## 
## 
## Biplot scores for constraining variables
## 
##              RDA1    RDA2     RDA3     RDA4     RDA5      RDA6
## initopen  0.43929  0.3636 -0.69197  0.03936 -0.16988 -0.002386
## block2   -0.49312 -0.5019 -0.44544  0.30490 -0.37820 -0.263238
## block3    0.60037 -0.2600  0.27165 -0.29023 -0.61364  0.066324
## block4    0.07944  0.2413 -0.37775 -0.17002  0.37101 -0.167656
## block5    0.34482 -0.3558  0.03162  0.49352  0.53510  0.438225
## block6    0.09038  0.3744  0.41332  0.28159  0.09141 -0.718450
## block7   -0.38567  0.5987  0.14166  0.16120 -0.30372  0.595132
## 
## 
## Centroids for factor constraints
## 
##              RDA1    RDA2     RDA3     RDA4     RDA5      RDA6
## initlog  -0.19057 -0.1577  0.30019 -0.01707  0.07370  0.001035
## initopen  0.19057  0.1577 -0.30019  0.01707 -0.07370 -0.001035
## block1   -0.25101 -0.1028 -0.03724 -0.82988  0.31671  0.052774
## block2   -0.52400 -0.5334 -0.47334  0.32400 -0.40189 -0.279725
## block3    0.63797 -0.2763  0.28866 -0.30841 -0.65207  0.070478
## block4    0.08441  0.2564 -0.40141 -0.18067  0.39425 -0.178156
## block5    0.36641 -0.3781  0.03360  0.52443  0.56861  0.465671
## block6    0.09604  0.3979  0.43921  0.29923  0.09713 -0.763447
## block7   -0.40983  0.6363  0.15053  0.17130 -0.32274  0.632406
## Permutation test for rda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## Model: rda(formula = ass.rel.t2 ~ init + block)
##          Df Variance      F Pr(>F)   
## init      1  0.06081 2.0739  0.015 * 
## block     6  0.29726 1.6897  0.005 **
## Residual  6  0.17593                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## rda(X = ass.rel.t2, Y = init, Z = block) 
## 
## Partitioning of variance:
##               Inertia Proportion
## Total         0.53400     1.0000
## Conditioned   0.29726     0.5567
## Constrained   0.06081     0.1139
## Unconstrained 0.17593     0.3295
## 
## Eigenvalues, and their contribution to the variance 
## after removing the contribution of conditiniong variables
## 
## Importance of components:
##                          RDA1    PC1     PC2     PC3     PC4     PC5     PC6
## Eigenvalue            0.06081 0.0547 0.03572 0.03141 0.02309 0.01636 0.01464
## Proportion Explained  0.25687 0.2311 0.15089 0.13267 0.09755 0.06910 0.06185
## Cumulative Proportion 0.25687 0.4879 0.63882 0.77149 0.86905 0.93815 1.00000
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                          RDA1
## Eigenvalue            0.06081
## Proportion Explained  1.00000
## Cumulative Proportion 1.00000
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  1.623197 
## 
## 
## Species scores
## 
##            RDA1        PC1        PC2        PC3        PC4        PC5
## acul   0.020175 -2.142e-02 -8.157e-03 -2.565e-02 -6.560e-03 -2.267e-02
## aicu   0.000000 -4.876e-17 -4.519e-17 -3.653e-17  6.908e-18 -1.928e-17
## arca   0.000000 -8.942e-18  4.164e-17  3.659e-17  9.181e-18 -7.967e-18
## ardy  -0.027831 -4.756e-02 -3.944e-02  8.675e-03 -1.186e-02  5.108e-03
## arsp   0.000000 -2.014e-17 -2.380e-19 -1.365e-17  1.358e-17  1.535e-17
## auel   0.000000 -4.771e-18 -4.237e-19 -5.032e-18 -3.204e-18 -1.865e-17
## bldr   0.077576 -1.896e-02  1.384e-02  1.648e-02  6.044e-03  1.461e-01
## blrd   0.000000 -8.146e-18  9.663e-18  1.578e-17 -1.302e-17  4.303e-18
## brdi   0.028032  2.184e-03 -2.188e-02 -3.219e-02  1.751e-02  5.284e-02
## brdr   0.000000  5.211e-20 -9.460e-19 -2.257e-18  2.614e-18  9.147e-19
## brpe   0.000000 -7.947e-19  4.558e-18 -2.427e-19  1.126e-17 -2.201e-17
## brru   0.000000  6.061e-35 -3.019e-34  1.792e-34 -1.096e-33  1.716e-33
## buse   0.000000  1.148e-34 -5.719e-34  3.395e-34 -2.077e-33  3.251e-33
## caer  -0.044190  2.898e-02 -2.379e-02 -2.399e-02  2.885e-02 -2.967e-02
## cagr   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## cahi  -0.067194 -7.302e-03 -1.008e-01 -7.009e-02  7.686e-02  2.540e-02
## casp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## cear   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chau   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## chei   0.093970  8.115e-02  7.788e-02 -2.052e-02  5.365e-02 -6.052e-02
## chps   0.156906 -6.527e-02 -4.068e-02  4.561e-04 -4.431e-02  2.951e-02
## crcl  -0.016809 -6.644e-03  7.075e-04 -5.708e-03  3.885e-02 -5.185e-03
## crco   0.079849  2.340e-02 -9.206e-02  2.134e-02 -4.315e-02  4.745e-02
## cusc  -0.050462 -2.884e-02 -1.858e-02 -5.884e-02  1.989e-01  2.863e-02
## cusp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## dagl   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## dosp  -0.053319 -2.094e-01 -1.574e-01 -2.948e-02 -5.637e-02 -3.437e-02
## ento   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## erau  -0.035104 -1.388e-02  1.477e-03 -1.192e-02  8.114e-02 -1.083e-02
## ercy   0.046162 -4.595e-02  4.546e-02 -3.595e-03 -6.486e-02  1.494e-02
## erra   0.033378 -3.459e-02 -1.612e-02 -4.611e-02 -1.184e-02 -4.033e-02
## ersp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gite   0.034332  5.867e-02  4.865e-02 -1.070e-02  1.463e-02 -6.301e-03
## gnte   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gobe  -0.070327 -2.086e-02  5.792e-02 -7.753e-02 -4.545e-02  7.220e-02
## gocy   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## gono   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## goro   0.192777 -2.313e-02  4.363e-02  2.883e-02  6.048e-02 -2.237e-02
## gosp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## haod   0.056933 -4.086e-02 -8.614e-02 -4.801e-02 -2.278e-02 -3.305e-03
## hygl   0.032852 -4.006e-02 -3.934e-02 -3.105e-02  9.185e-02 -7.827e-02
## hypi   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## hypo  -0.136055 -1.149e-01 -6.122e-02 -1.281e-02  2.658e-02  3.954e-02
## jubu   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## laro  -0.062935 -5.030e-02 -1.204e-02  4.898e-02 -3.378e-02 -6.106e-02
## ledu   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## lele   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## loef   0.028032  4.791e-02  3.972e-02 -8.738e-03  1.194e-02 -5.144e-03
## misp   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mite  -0.113449  2.772e-01 -6.553e-02  2.380e-02 -5.406e-02 -9.228e-03
## momo   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mopa   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## niro   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## omco   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## orsp  -0.024822 -1.934e-03  1.938e-02  2.850e-02 -1.550e-02 -4.679e-02
## pala   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## peai   0.142682  1.789e-01 -2.923e-02  1.335e-01  2.529e-02  2.227e-02
## pedu   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## phsu   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## plde   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poar   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poca  -0.014943  8.686e-02  3.030e-02  8.356e-02  2.960e-02  3.563e-02
## pocap -0.004362  6.952e-02  4.738e-02 -7.135e-03 -1.505e-02  4.273e-02
## poce  -0.024822 -1.934e-03  1.938e-02  2.850e-02 -1.550e-02 -4.679e-02
## pogn   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pole  -0.026285  1.014e-01 -6.193e-02 -1.139e-01 -5.895e-02 -6.553e-02
## pomu   0.247365 -1.682e-01  6.404e-02  2.052e-01 -3.013e-02 -6.595e-03
## pter   0.073175 -7.558e-03 -3.227e-02 -5.003e-02 -4.913e-02  3.754e-02
## ptga   0.019822  1.544e-03 -1.547e-02 -2.276e-02  1.238e-02  3.736e-02
## ptob   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## rhla  -0.055929  8.999e-02 -7.097e-02 -6.003e-02 -1.719e-02 -3.763e-02
## rhpy  -0.035682 -2.780e-03  2.785e-02  4.097e-02 -2.229e-02 -6.726e-02
## rhsp  -0.056525  1.354e-02  1.937e-01 -1.522e-01 -3.834e-02  2.451e-02
## ry     0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## scna   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## sino  -0.035104 -1.388e-02  1.477e-03 -1.192e-02  8.114e-02 -1.083e-02
## sool   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## stfi  -0.024822 -1.934e-03  1.938e-02  2.850e-02 -1.550e-02 -4.679e-02
## stpi   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## thma   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## trcy  -0.141493  8.767e-02 -1.282e-01  4.651e-02 -2.340e-02  3.479e-02
## tris   0.032930 -5.298e-02  4.179e-02  3.534e-02  1.012e-02  2.215e-02
## tror  -0.121334  1.672e-02  4.009e-02  7.411e-02  5.603e-02 -1.665e-02
## trpi   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## waac  -0.130919 -3.799e-02  1.255e-01 -6.015e-02 -5.416e-02 -2.217e-02
## wagr   0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## x      0.000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## 
## 
## Site scores (weighted sums of species scores)
## 
##          RDA1      PC1      PC2      PC3     PC4      PC5
## sit1  -0.5054 -0.21824  0.68560 -0.67128 -0.2526  0.14674
## sit2   0.5054  0.21824 -0.68560  0.67128  0.2526 -0.14674
## sit3  -0.3427 -0.74139 -0.61475  0.13523 -0.1848  0.07961
## sit4   0.3427  0.74139  0.61475 -0.13523  0.1848 -0.07961
## sit5  -0.4597 -0.17148  0.01826 -0.14731  1.0027 -0.13382
## sit6   0.4597  0.17148 -0.01826  0.14731 -1.0027  0.13382
## sit7  -0.2952  0.44952  0.20946  0.59934  0.1539  0.52410
## sit8   0.2952 -0.44952 -0.20946 -0.59934 -0.1539 -0.52410
## sit9  -0.7338  0.69798 -0.55047 -0.46560 -0.1334 -0.29185
## sit10  0.7338 -0.69798  0.55047  0.46560  0.1334  0.29185
## sit11 -0.3003 -0.03380  0.33862  0.49814 -0.2710 -0.81776
## sit12  0.3003  0.03380 -0.33862 -0.49814  0.2710  0.81776
## sit13 -0.3996  0.01741 -0.08672  0.05148 -0.3149  0.49297
## sit14  0.3996 -0.01741  0.08672 -0.05148  0.3149 -0.49297
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##          RDA1      PC1      PC2      PC3     PC4      PC5
## con1  -0.4338 -0.21824  0.68560 -0.67128 -0.2526  0.14674
## con2   0.4338  0.21824 -0.68560  0.67128  0.2526 -0.14674
## con3  -0.4338 -0.74139 -0.61475  0.13523 -0.1848  0.07961
## con4   0.4338  0.74139  0.61475 -0.13523  0.1848 -0.07961
## con5  -0.4338 -0.17148  0.01826 -0.14731  1.0027 -0.13382
## con6   0.4338  0.17148 -0.01826  0.14731 -1.0027  0.13382
## con7  -0.4338  0.44952  0.20946  0.59934  0.1539  0.52410
## con8   0.4338 -0.44952 -0.20946 -0.59934 -0.1539 -0.52410
## con9  -0.4338  0.69798 -0.55047 -0.46560 -0.1334 -0.29185
## con10  0.4338 -0.69798  0.55047  0.46560  0.1334  0.29185
## con11 -0.4338 -0.03380  0.33862  0.49814 -0.2710 -0.81776
## con12  0.4338  0.03380 -0.33862 -0.49814  0.2710  0.81776
## con13 -0.4338  0.01741 -0.08672  0.05148 -0.3149  0.49297
## con14  0.4338 -0.01741  0.08672 -0.05148  0.3149 -0.49297
## 
## 
## Biplot scores for constraining variables
## 
##       RDA1 PC1 PC2 PC3 PC4 PC5
## Yopen    1   0   0   0   0   0
## [1] 0.1095098
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: rda(X = ass.rel.t2, Y = init, Z = block)
##          Df Variance      F Pr(>F)  
## Model     1  0.06081 2.0739  0.052 .
## Residual  6  0.17593                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Composition dissimilarity * 2020 - 2022 *

## 
## Call:
## rda(formula = ass.rel.t012 ~ init + Condition(block + time)) 
## 
## Partitioning of variance:
##               Inertia Proportion
## Total         0.56175     1.0000
## Conditioned   0.24900     0.4433
## Constrained   0.03416     0.0608
## Unconstrained 0.27860     0.4959
## 
## Eigenvalues, and their contribution to the variance 
## after removing the contribution of conditiniong variables
## 
## Importance of components:
##                          RDA1    PC1     PC2     PC3     PC4     PC5     PC6
## Eigenvalue            0.03416 0.0397 0.02773 0.02383 0.02008 0.01758 0.01589
## Proportion Explained  0.10921 0.1269 0.08867 0.07618 0.06419 0.05620 0.05080
## Cumulative Proportion 0.10921 0.2362 0.32482 0.40101 0.46520 0.52139 0.57219
##                           PC7     PC8     PC9    PC10    PC11     PC12     PC13
## Eigenvalue            0.01486 0.01326 0.01138 0.01024 0.01004 0.008831 0.008658
## Proportion Explained  0.04752 0.04240 0.03637 0.03273 0.03211 0.028235 0.027685
## Cumulative Proportion 0.61971 0.66211 0.69848 0.73121 0.76332 0.791553 0.819237
##                           PC14     PC15     PC16     PC17     PC18     PC19
## Eigenvalue            0.007353 0.006713 0.005727 0.005255 0.004236 0.003939
## Proportion Explained  0.023511 0.021466 0.018313 0.016802 0.013546 0.012596
## Cumulative Proportion 0.842748 0.864213 0.882526 0.899328 0.912874 0.925469
##                           PC20     PC21     PC22     PC23     PC24     PC25
## Eigenvalue            0.003339 0.003025 0.002930 0.002555 0.002392 0.002270
## Proportion Explained  0.010676 0.009671 0.009367 0.008169 0.007648 0.007257
## Cumulative Proportion 0.936145 0.945816 0.955183 0.963353 0.971001 0.978257
##                           PC26     PC27     PC28      PC29      PC30      PC31
## Eigenvalue            0.001666 0.001540 0.001124 0.0009868 0.0006454 0.0005422
## Proportion Explained  0.005326 0.004923 0.003594 0.0031552 0.0020636 0.0017336
## Cumulative Proportion 0.983583 0.988506 0.992100 0.9952553 0.9973189 0.9990524
##                            PC32
## Eigenvalue            0.0002963
## Proportion Explained  0.0009476
## Cumulative Proportion 1.0000000
## 
## Accumulated constrained eigenvalues
## Importance of components:
##                          RDA1
## Eigenvalue            0.03416
## Proportion Explained  1.00000
## Cumulative Proportion 1.00000
## 
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:  2.190697 
## 
## 
## Species scores
## 
##             RDA1        PC1        PC2        PC3        PC4        PC5
## acul   0.0755447 -1.434e-02  7.884e-02 -1.593e-03  8.467e-02  2.948e-02
## aicu   0.0138510 -5.744e-03 -2.254e-02 -3.823e-02 -1.389e-02 -2.313e-02
## arca   0.0000000 -9.271e-18 -2.981e-17 -4.432e-18  1.136e-17 -5.090e-17
## ardy   0.0061606 -3.600e-02  2.215e-02  4.187e-02  7.286e-03 -9.784e-05
## arsp   0.0086051  7.583e-03  1.074e-02  5.817e-03  9.230e-03  2.952e-03
## auel   0.0000000 -4.520e-17 -3.721e-17  1.033e-17  1.268e-17  4.689e-17
## bldr   0.0415582  1.024e-01 -5.538e-02  3.641e-02  6.442e-02 -6.221e-03
## blrd   0.0183399  5.422e-03  2.508e-02  2.563e-03  1.299e-02  1.353e-02
## brdi   0.0119918  9.759e-03  2.022e-03 -2.539e-03  2.480e-03 -4.325e-03
## brdr   0.0000000 -7.790e-18 -1.962e-17  2.861e-17 -4.322e-18 -1.126e-17
## brpe  -0.0548396  5.401e-02 -1.678e-02 -2.939e-02 -2.420e-02 -8.146e-03
## brru   0.0000000  2.293e-17 -1.930e-17 -3.465e-18 -2.242e-17 -2.590e-17
## buse  -0.0161408 -3.475e-03  1.721e-02 -2.478e-04 -3.937e-03  9.186e-03
## caer  -0.0452451 -6.696e-02  9.202e-02 -1.616e-01  4.003e-02 -3.100e-02
## cagr  -0.0551717 -3.321e-02 -1.867e-02 -6.880e-02 -7.224e-02 -3.597e-02
## cahi  -0.0257702 -1.225e-02  5.902e-02  2.155e-02 -1.343e-02 -6.753e-03
## casp   0.0015456 -1.514e-02  8.747e-03  2.929e-02  6.102e-02  1.539e-02
## cear   0.0183399  9.032e-03  3.008e-03  7.984e-03 -3.408e-02 -4.754e-02
## chau   0.0711459  2.373e-02  4.398e-02 -1.230e-01 -5.926e-02 -5.960e-02
## chei   0.0401993  7.383e-03 -2.523e-02  6.336e-03 -3.648e-02  4.773e-02
## chps   0.1934722 -2.173e-02  3.508e-02  7.579e-02  1.279e-01  3.187e-02
## crcl  -0.0071908 -4.839e-02  6.939e-03 -3.034e-02 -3.984e-03  1.194e-03
## crco   0.0909945  1.388e-02  3.934e-02  1.367e-01  3.854e-02 -7.024e-02
## cusc  -0.0215871 -7.383e-02  3.359e-02 -5.510e-02 -1.432e-02  1.409e-02
## cusp   0.0102164 -1.830e-03  2.398e-02 -2.390e-03  8.359e-03  1.051e-02
## dagl  -0.0002086 -1.267e-02 -3.465e-03  4.740e-03  5.809e-03  5.524e-03
## dosp  -0.0228091 -7.269e-02  7.834e-02  1.285e-01 -1.337e-02 -7.897e-02
## ento   0.0000000 -3.969e-18 -6.278e-18 -1.112e-17 -2.983e-19  5.476e-18
## erau  -0.0150169 -3.738e-02  1.412e-02 -2.351e-02 -7.513e-03  8.236e-03
## ercy  -0.0354798 -1.267e-02 -4.291e-02  1.901e-02 -9.685e-03 -3.599e-02
## erra   0.0227357 -1.122e-02  2.726e-02  2.923e-03 -1.143e-02  7.106e-03
## ersp  -0.0084795 -7.754e-03  3.923e-03 -1.604e-03 -1.582e-02  6.429e-03
## gite  -0.0130745  3.665e-02 -9.580e-02 -1.441e-02  6.247e-02  9.662e-03
## gnte   0.0449263  6.207e-03  3.387e-02 -5.636e-02 -6.490e-02 -1.134e-02
## gobe  -0.0389147 -1.221e-02  2.324e-03  2.536e-02  1.356e-02  2.056e-01
## gocy  -0.0005179  3.628e-03  4.776e-02  8.507e-03  3.434e-04 -3.315e-02
## gono   0.0000000  5.463e-18  1.998e-18 -1.741e-18 -8.489e-18  6.585e-19
## goro   0.1287318 -6.246e-02 -1.634e-01  6.953e-02 -2.130e-01  3.958e-02
## gosp   0.0180699  4.609e-03  2.551e-02 -1.270e-02  2.529e-02 -1.785e-02
## haod   0.0085083  2.446e-02  2.793e-03  2.387e-02  5.230e-02 -9.922e-03
## hygl   0.1305498  2.905e-03  1.209e-01  8.285e-02 -5.754e-02  2.925e-02
## hypi  -0.0150169  1.725e-02  4.290e-03 -2.482e-02 -1.172e-02 -1.151e-02
## hypo  -0.0582026 -3.197e-02  8.701e-02  6.189e-02 -2.330e-02  3.009e-02
## jubu   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## laro  -0.1001787 -5.003e-02  2.308e-02  1.384e-02  3.061e-02 -5.441e-02
## ledu   0.0018314  2.583e-02  1.169e-03  1.944e-02  9.125e-03  1.162e-02
## lele   0.0170846  3.174e-03  7.089e-03  5.462e-03  4.878e-04  1.083e-02
## loef   0.0119918  3.267e-03 -7.624e-04  3.625e-03 -1.161e-02  1.227e-02
## misp  -0.0677312 -9.016e-02  5.996e-02 -1.150e-01 -4.657e-02 -5.294e-02
## mite  -0.0485321  2.148e-01 -5.397e-02 -5.310e-02  3.580e-02  5.387e-02
## momo   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## mopa   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## niro  -0.0157499  3.081e-02 -8.896e-03  1.270e-02 -3.637e-03 -2.570e-03
## omco   0.0135532 -9.910e-03 -1.428e-02  4.070e-02 -3.485e-04  1.916e-02
## orsp  -0.0106186 -1.238e-03 -5.492e-03 -1.036e-02  5.195e-05 -5.661e-03
## pala   0.0086051  7.583e-03  1.074e-02  5.817e-03  9.230e-03  2.952e-03
## peai   0.1028116  1.736e-01 -2.005e-01 -3.081e-02  1.369e-02  2.640e-02
## pedu   0.0121694  1.072e-02  1.520e-02  8.226e-03  1.305e-02  4.174e-03
## phsu  -0.0111369  2.179e-02 -6.290e-03  8.980e-03 -2.571e-03 -1.818e-03
## plde   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## poar   0.0027883 -1.889e-02  1.721e-02 -6.389e-02 -1.656e-02  6.553e-03
## poca   0.0062046  8.808e-02 -6.471e-02 -2.795e-03 -6.767e-02  1.822e-02
## pocap  0.0045075  8.631e-03  3.229e-02 -3.460e-03  2.430e-02  1.976e-02
## poce  -0.0106186 -1.238e-03 -5.492e-03 -1.036e-02  5.195e-05 -5.661e-03
## pogn   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## pole  -0.0298641  1.578e-01  3.998e-02 -1.008e-01  5.034e-03 -8.070e-03
## pomu   0.1993083 -3.328e-01 -2.100e-01  8.524e-03  8.303e-02 -6.531e-02
## pter   0.0313036 -8.137e-03 -1.022e-02 -6.534e-03  8.000e-03 -2.134e-02
## ptga   0.0518059  6.918e-03  1.242e-01 -6.995e-02 -3.267e-02  1.731e-02
## ptob   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## rhla  -0.0192925  8.336e-02 -3.393e-02 -1.024e-01  7.481e-02 -1.291e-02
## rhpy  -0.0745613  1.446e-02 -4.187e-02 -1.141e-02 -1.525e-01 -5.915e-02
## rhsp  -0.0241808  1.203e-02  1.080e-01  8.131e-02 -8.713e-02  9.129e-02
## ry     0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## scna   0.0187550  6.243e-03  1.596e-02 -2.118e-02 -3.013e-02 -1.789e-02
## sino  -0.0150169 -3.738e-02  1.412e-02 -2.351e-02 -7.513e-03  8.236e-03
## sool  -0.0138136 -1.380e-02 -7.854e-03  7.919e-03 -7.863e-03 -1.252e-02
## stfi  -0.0194231 -8.594e-03  6.600e-03 -1.169e-02  1.458e-02 -5.568e-03
## stpi   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## thma   0.0000000  0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
## trcy  -0.2242559  2.113e-01  1.067e-02  1.038e-01  8.545e-03 -2.070e-01
## tris   0.0402117 -3.209e-02  1.911e-02 -8.204e-03 -1.746e-02 -5.635e-02
## tror  -0.1544269 -6.830e-02 -3.490e-02 -8.891e-02  5.911e-02  1.022e-02
## trpi  -0.0437537  1.461e-02 -2.404e-02  7.331e-02  2.214e-02  1.813e-03
## waac  -0.1356532 -3.713e-02  4.244e-02 -4.687e-02  6.527e-03 -2.002e-02
## wagr  -0.0065153 -8.588e-03  6.302e-03  2.518e-03  2.094e-03  3.230e-02
## x     -0.0133788 -5.871e-03  2.583e-02 -1.927e-04  4.224e-02  1.243e-03
## 
## 
## Site scores (weighted sums of species scores)
## 
##           RDA1      PC1       PC2        PC3       PC4       PC5
## row1  -0.26449 -0.11731  0.072142 -0.1786127  0.003365 -0.186582
## row2   0.32774 -0.25141 -0.315084 -0.1260442 -0.262245 -0.115106
## row3  -0.46922  0.11517 -0.268870 -0.3406530  0.698008 -0.329868
## row4   0.27897  0.21540 -0.700246 -0.4535292 -0.020427  0.244714
## row5  -0.39084  0.31262 -0.122452  0.2712654 -0.103664 -0.151995
## row6   0.25593  0.23780  0.111247  0.0077685 -0.106470 -0.024464
## row7  -0.28933 -0.30912  0.156394 -0.0639535 -0.630565  0.256295
## row8   0.22871  0.03594  0.165376  0.0061877  0.158766  0.186223
## row9  -0.14623  0.13592 -0.190179  0.4505984 -0.907619  0.085541
## row10  0.63059 -0.41985 -0.221834  0.3855205  0.244325  0.427107
## row11  0.01622 -0.02003  0.033700 -0.0067724  0.106785  0.178492
## row12  0.21309  0.29790  0.422079  0.2285040  0.362588  0.115951
## row13 -0.43323 -0.28242  0.464241 -0.0511444  0.557801  0.003577
## row14  0.04211  0.04939  0.393486 -0.1291351 -0.100648 -0.689887
## row15 -0.20945 -0.53535 -0.191302 -0.4967942  0.149392 -0.088724
## row16  0.48864  0.11795  0.004154  0.4405109  0.555465 -0.021861
## row17 -0.29523  0.06965 -0.199387  0.6302322  0.230438  0.049894
## row18  0.45621  0.06090  0.509639 -0.8875723 -0.461571  0.207254
## row19 -0.49980  0.66131 -0.190927  0.2725541 -0.078050 -0.055167
## row20  0.45815  0.10541  0.194447  0.2727072  0.304887  0.350394
## row21 -0.15821 -0.13366 -0.241084 -0.0026471  0.350202 -0.173006
## row22  0.08433  0.09447  0.730090 -0.1782412  0.173954  0.148209
## row23 -0.87372  0.38836  0.096577 -0.5587161 -0.263845 -0.259079
## row24  0.54389 -0.30905 -0.033889  0.1578082  0.214942  0.339469
## row25 -0.19692 -0.33779 -0.192198  0.1937864 -0.192412 -0.306358
## row26  0.31799 -0.17579 -0.145749 -0.0141532 -0.348512  0.314036
## row27 -0.30510 -0.17289 -0.395816  0.0233742 -0.006757  0.371098
## row28  0.18924  0.16647  0.055446  0.1471508 -0.628134 -0.876158
## row29 -0.59762  0.26775  0.658661  0.2113891 -0.436792  0.732128
## row30  0.25517  0.51838 -0.228572  0.1495511 -0.009184 -0.319855
## row31 -0.23382 -0.55322  0.680356  0.9493292 -0.119289 -0.517903
## row32  0.26309  0.09208 -0.021492  0.1021932 -0.327159  0.345909
## row33 -0.22411 -0.84145  0.317819 -0.5291453 -0.169109  0.185397
## row34  0.40068 -0.47569 -0.310134 -0.2951499  0.152405 -0.304166
## row35 -0.26668  0.40753 -0.725183  0.0061752 -0.074115 -0.128422
## row36  0.40119 -0.09516 -0.085593  0.2324789  0.021757 -0.289300
## row37 -0.68266  0.77489  0.513757 -0.4592464  0.421879 -0.215783
## row38  0.52814 -0.57026 -0.164432  0.0240354  0.290319 -0.377254
## row39 -0.49625 -0.03940 -0.174828 -0.3297816  0.001654 -0.180206
## row40  0.14586  0.27510  0.056997 -0.0715832  0.069898 -0.121915
## row41 -0.08197  0.20944 -0.101421  0.0087625  0.462695  0.730672
## row42  0.58896  0.03001 -0.415936  0.0009919 -0.284957  0.460698
## 
## 
## Site constraints (linear combinations of constraining variables)
## 
##         RDA1      PC1       PC2        PC3       PC4       PC5
## row1  -0.338 -0.11731  0.072142 -0.1786127  0.003365 -0.186582
## row2   0.338 -0.25141 -0.315084 -0.1260442 -0.262245 -0.115106
## row3  -0.338  0.11517 -0.268870 -0.3406530  0.698008 -0.329868
## row4   0.338  0.21540 -0.700246 -0.4535292 -0.020427  0.244714
## row5  -0.338  0.31262 -0.122452  0.2712654 -0.103664 -0.151995
## row6   0.338  0.23780  0.111247  0.0077685 -0.106470 -0.024464
## row7  -0.338 -0.30912  0.156394 -0.0639535 -0.630565  0.256295
## row8   0.338  0.03594  0.165376  0.0061877  0.158766  0.186223
## row9  -0.338  0.13592 -0.190179  0.4505984 -0.907619  0.085541
## row10  0.338 -0.41985 -0.221834  0.3855205  0.244325  0.427107
## row11 -0.338 -0.02003  0.033700 -0.0067724  0.106785  0.178492
## row12  0.338  0.29790  0.422079  0.2285040  0.362588  0.115951
## row13 -0.338 -0.28242  0.464241 -0.0511444  0.557801  0.003577
## row14  0.338  0.04939  0.393486 -0.1291351 -0.100648 -0.689887
## row15 -0.338 -0.53535 -0.191302 -0.4967942  0.149392 -0.088724
## row16  0.338  0.11795  0.004154  0.4405109  0.555465 -0.021861
## row17 -0.338  0.06965 -0.199387  0.6302322  0.230438  0.049894
## row18  0.338  0.06090  0.509639 -0.8875723 -0.461571  0.207254
## row19 -0.338  0.66131 -0.190927  0.2725541 -0.078050 -0.055167
## row20  0.338  0.10541  0.194447  0.2727072  0.304887  0.350394
## row21 -0.338 -0.13366 -0.241084 -0.0026471  0.350202 -0.173006
## row22  0.338  0.09447  0.730090 -0.1782412  0.173954  0.148209
## row23 -0.338  0.38836  0.096577 -0.5587161 -0.263845 -0.259079
## row24  0.338 -0.30905 -0.033889  0.1578082  0.214942  0.339469
## row25 -0.338 -0.33779 -0.192198  0.1937864 -0.192412 -0.306358
## row26  0.338 -0.17579 -0.145749 -0.0141532 -0.348512  0.314036
## row27 -0.338 -0.17289 -0.395816  0.0233742 -0.006757  0.371098
## row28  0.338  0.16647  0.055446  0.1471508 -0.628134 -0.876158
## row29 -0.338  0.26775  0.658661  0.2113891 -0.436792  0.732128
## row30  0.338  0.51838 -0.228572  0.1495511 -0.009184 -0.319855
## row31 -0.338 -0.55322  0.680356  0.9493292 -0.119289 -0.517903
## row32  0.338  0.09208 -0.021492  0.1021932 -0.327159  0.345909
## row33 -0.338 -0.84145  0.317819 -0.5291453 -0.169109  0.185397
## row34  0.338 -0.47569 -0.310134 -0.2951499  0.152405 -0.304166
## row35 -0.338  0.40753 -0.725183  0.0061752 -0.074115 -0.128422
## row36  0.338 -0.09516 -0.085593  0.2324789  0.021757 -0.289300
## row37 -0.338  0.77489  0.513757 -0.4592464  0.421879 -0.215783
## row38  0.338 -0.57026 -0.164432  0.0240354  0.290319 -0.377254
## row39 -0.338 -0.03940 -0.174828 -0.3297816  0.001654 -0.180206
## row40  0.338  0.27510  0.056997 -0.0715832  0.069898 -0.121915
## row41 -0.338  0.20944 -0.101421  0.0087625  0.462695  0.730672
## row42  0.338  0.03001 -0.415936  0.0009919 -0.284957  0.460698
## 
## 
## Biplot scores for constraining variables
## 
##          RDA1 PC1 PC2 PC3 PC4 PC5
## initopen    1   0   0   0   0   0
## 
## 
## Centroids for factor constraints
## 
##            RDA1 PC1 PC2 PC3 PC4 PC5
## initlog  -0.338   0   0   0   0   0
## initopen  0.338   0   0   0   0   0
## [1] 0.05628909
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: rda(formula = ass.rel.t012 ~ init + Condition(block + time))
##          Df Variance      F Pr(>F)    
## Model     1 0.034157 3.9233  0.001 ***
## Residual 32 0.278596                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Q2 Why are plant communities in fallen log patches different from patches in the open?

Overview of results

  • Loading of P and pH on PCA are similar. For subsequence analyses, only P is included as we can infer the effect of pH from it.
  • Soil C is significantly higher in log patches than open patches.
  • Plant abundance has a variable relationship with different nutrient elements. In 2020, we found more plants in C rich area. In 2021, we found more plants in P rich area. In 2022, plants abundance tend to drop with N but increase in C and base cations. Overall (2020-2022), N tends to have a negative relationship with plant abundance.
  • Relationship between plant diversity and soil elements are variable.
  • There are no significant relationships found between plant composition and soil elements.

Nutrient comparison Total soil carbon is significantly higher in log patches than open patches (Wilcoxon rank sum test, p<0.05).

2020-2022 There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix. 1. Abundance – the inclusive model includes all nutrient elements as additive fixed factors with block and year as random terms. As suggested by this inclusive model, the abundance of plants is significantly more likely to increase with soil carbon (C: p=0.004) level but decrease with plant-available nitrogen (NH4N: p<0.001) and basic exchangeable cations (CEC: p=0.008). [This seems to be a bit confounding as it does not make sense biologically. Maybe an artefact of collinearity?] 2. Diversity – the probability of having a positive Shannon diversity index in the plant community is significantly higher with higher levels of plant-available nitrogen (NO3N: p < 0.05) and potassium (K: p < 0.05) but lower with higher soil phosphorus (P: p <0.05). A similar pattern is observed for the level of diversity. The presented model has a structure of “diversity ~ elements (additive) + (1|block) + (1|year)”. 3. Composition – The composition of plant communities is not significantly explained by any of the nutrient elements. See print(en.nutrient).

2020 There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix. 1. Abundance – the inclusive model includes all nutrient elements as additive fixed factors with block and year as random terms. As suggested by this inclusive model, the abundance of plants is significantly more likely to increase with soil carbon (C: p=0.04) level. 2. Diversity – the probability of having a positive Shannon diversity index in the plant community is significantly higher with higher levels of plant-available nitrogen (NO3N: p < 0.05) and potassium (K: p < 0.05). A similar pattern is observed for the level of diversity. The presented model has a structure of “diversity ~ elements (additive) + (1|block)”. 3. Composition – The composition of plant communities is not significantly explained by any of the nutrient elements. See print(en.nutrient).

2021 There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix. 1. Abundance – the inclusive model includes all nutrient elements as additive fixed factors with block and year as random terms. As suggested by this inclusive model, the abundance of plants is significantly more likely to increase with plant-available phosphorus (P: p=0.03) level but decrease with plant-available nitrogen (NH4N: p=0.009). [Same comment] 2. Diversity – the level of plant diversity as represented by the Shannon diversity index and the probability of having a positive Shannon diversity index in the plant community are not significantly explained by any soil nutrient elements. The presented model has a structure of “diversity ~ elements (additive) + (1|block)”. 3. Composition – The composition of plant communities is not significantly explained by any of the nutrient elements. See print(en.nutrient).

2022 There are three response variables: count (Poisson), Shannon diversity index (hurdle: binomial and Gaussian), and composition matrix. 1. Abundance – the inclusive model includes all nutrient elements as additive fixed factors with block and year as random terms. As suggested by this inclusive model, the abundance of plants is significantly more likely to decrease with plant-available nitrogen (NH4N: p<0.001) and potassium (K: p=0.03). [Same comment] 2. Diversity – the level of plant diversity as represented by the Shannon diversity index and the probability of having a positive Shannon diversity index in the plant community are not significantly explained by any soil nutrient elements. The presented model has a structure of “diversity ~ elements (additive) + (1|block)”. 3. Composition – The composition of plant communities is not significantly explained by any of the nutrient elements. See print(en.nutrient).

Statistical Methods

  1. We performed a principal component analysis with correlation matrix on seven soil properties to understand the multivariate relationships among them.
  2. We used the Wilcoxon rank sum test to compare soil nutrient levels between log and open plots.
  3. Effect of soil nutrient on plant abundance.

This analysis involves three types of data: (1) plant counts of all plots in 2020 before the experiment setup differentiated by the initial treatment “log” and “open”; (2) plant counts from insitu_log and insitu_open plots in 2021 and 2022 respectively, including species with unknown identity; and (3) soil nutrient data collected from one random log plot and one random open plot in each block.

Since independent nutrient variables were sampled at block level, this part of the analysis does not include block as the random term to avoid singularity issues. We used a generalized linear model (GLM) with a Poisson distribution structure to model the effect of different soil components on the abundance of plant individuals (data from 2020, 2021, and 2022). Additionally, we performed model dredging (using the MuMln package) to compare the best fitting model, the biologically sensible model, and the inclusive model with all nutrient elements.

To avoid pseudo-replication, when pulling count data from 2020 to 2022 together, we included time as a random term.

  1. Effect of soil nutrient on plant diversity (Shannon diversity index = SDI) This analysis involves three types of data: (1) plant count for each species of all plots in 2020 before the experiment setup differentiated by the initial treatment “log” and “open”; and (2) plant count for each species from insitu_log and insitu_open plots in 2021 and 2022 respectively. All data used for diversity comparison excludes species with unknown identity. (3) soil nutrient data. We used the Shannon diversity index to describe the plant species diversity for each transect at the plot level. Similarly, we used a hurdle model to model the effect of soil properties on plant diversity.
  1. Model the probability of zero and non-zero Shannon diversity with a logistic regression. GLM for single-year data and GLMER for 2020-2022 data with year as random terms in 2020-2022 data.
  2. Model the non-zero Shannon diversity with an LM for single-year data and LMER for 2020-2022 data with year as a random term since the distribution of non-zero data has an approximal Gaussian distribution.
  3. We also performed model dredging (using the MuMln package) to compare the best-fitting model, the biologically sensible model, and the inclusive model with all nutrient elements.
  1. We performed similar NMDS analyses as described in the previous section. We then used envfit analyses from the Vegan package to fit soil components onto the nondimensional space.

Analysis

## Classes and Methods for R originally developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University (2002-2015),
## by and under the direction of Simon Jackman.
## hurdle and zeroinfl functions by Achim Zeileis.
## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.6
## Current TMB version is 1.9.10
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)
## 
## Attaching package: 'AICcmodavg'
## The following object is masked from 'package:lme4':
## 
##     checkConv
## The following object is masked from 'package:labdsv':
## 
##     importance

Nutrient composition comparison between log and open
  • Organic carbon in soil is significantly higher in fallen log patches. 
##        element    w    p_value
## 1            N 41.5 0.03126054
## 2            P 26.5 0.82963146
## 3            K 26.0 0.90151515
## 4            C 42.5 0.02518656
## 5           pH 29.5 0.56096176
## 6          CEC 30.0 0.53496503
## 7 conductivity 30.5 0.47929050
Abundance ~ nutrient composition
  Best model Selected model Inclusive model (all years)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 1.82 0.02 1.78 – 1.86 <0.001 1.82 0.02 1.78 – 1.86 <0.001 1.82 0.02 1.78 – 1.86 <0.001
N -0.08 0.02 -0.13 – -0.03 0.001 -0.08 0.03 -0.14 – -0.02 0.007 -0.08 0.03 -0.14 – -0.02 0.007
C 0.05 0.03 -0.02 – 0.12 0.149 0.06 0.04 -0.01 – 0.13 0.100 0.06 0.04 -0.01 – 0.13 0.100
CEC -0.06 0.03 -0.12 – -0.00 0.044 -0.08 0.03 -0.14 – -0.01 0.024 -0.08 0.03 -0.14 – -0.01 0.024
P 0.04 0.03 -0.01 – 0.09 0.151 0.04 0.03 -0.01 – 0.09 0.151
K -0.01 0.03 -0.08 – 0.05 0.703 -0.01 0.03 -0.08 – 0.05 0.703
Observations 391 391 391
R2 Nagelkerke 0.039 0.045 0.045
  Best model Selected model Inclusive model (all years)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 1.82 0.02 1.78 – 1.86 <0.001 1.82 0.02 1.78 – 1.86 <0.001 1.82 0.02 1.78 – 1.86 <0.001
N -0.08 0.02 -0.13 – -0.03 0.001 -0.08 0.03 -0.14 – -0.02 0.007 -0.08 0.03 -0.14 – -0.02 0.007
C 0.05 0.03 -0.02 – 0.12 0.149 0.06 0.04 -0.01 – 0.13 0.100 0.06 0.04 -0.01 – 0.13 0.100
CEC -0.06 0.03 -0.12 – -0.00 0.044 -0.08 0.03 -0.14 – -0.01 0.024 -0.08 0.03 -0.14 – -0.01 0.024
P 0.04 0.03 -0.01 – 0.09 0.151 0.04 0.03 -0.01 – 0.09 0.151
K -0.01 0.03 -0.08 – 0.05 0.703 -0.01 0.03 -0.08 – 0.05 0.703
Observations 391 391 391
R2 Nagelkerke 0.039 0.045 0.045
  Best model Selected inclusive model (2022)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 2.18 0.04 2.11 – 2.26 <0.001 2.18 0.04 2.10 – 2.26 <0.001
N -0.16 0.05 -0.25 – -0.06 0.001 -0.18 0.06 -0.30 – -0.07 0.002
P -0.08 0.04 -0.17 – -0.00 0.052 -0.07 0.05 -0.16 – 0.02 0.149
C 0.19 0.07 0.04 – 0.33 0.009 0.16 0.07 0.02 – 0.29 0.021
CEC -0.15 0.06 -0.27 – -0.04 0.011 -0.13 0.06 -0.25 – -0.01 0.032
conductivity -0.08 0.05 -0.17 – 0.01 0.101
K -0.05 0.06 -0.17 – 0.06 0.336
Observations 84 84
R2 Nagelkerke 0.263 0.246
  Best model Selected inclusive model (2022)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 2.18 0.04 2.11 – 2.26 <0.001 2.18 0.04 2.10 – 2.26 <0.001
N -0.16 0.05 -0.25 – -0.06 0.001 -0.18 0.06 -0.30 – -0.07 0.002
P -0.08 0.04 -0.17 – -0.00 0.052 -0.07 0.05 -0.16 – 0.02 0.149
C 0.19 0.07 0.04 – 0.33 0.009 0.16 0.07 0.02 – 0.29 0.021
CEC -0.15 0.06 -0.27 – -0.04 0.011 -0.13 0.06 -0.25 – -0.01 0.032
conductivity -0.08 0.05 -0.17 – 0.01 0.101
K -0.05 0.06 -0.17 – 0.06 0.336
Observations 84 84
R2 Nagelkerke 0.263 0.246
  Best model Selected model Inclusive model (2021)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 2.00 0.04 1.91 – 2.08 <0.001 2.00 0.04 1.91 – 2.08 <0.001 2.00 0.04 1.92 – 2.08 <0.001
K -0.08 0.05 -0.18 – 0.02 0.100 -0.10 0.06 -0.22 – 0.03 0.132 -0.09 0.06 -0.22 – 0.03 0.154
N -0.09 0.05 -0.19 – 0.01 0.073 -0.10 0.06 -0.23 – 0.02 0.105 -0.10 0.06 -0.23 – 0.02 0.098
P 0.11 0.04 0.03 – 0.19 0.009 0.11 0.04 0.03 – 0.19 0.009 0.12 0.04 0.03 – 0.20 0.009
C 0.02 0.06 -0.10 – 0.13 0.753 0.04 0.08 -0.12 – 0.19 0.598
CEC -0.03 0.07 -0.16 – 0.10 0.664
Observations 86 86 86
R2 Nagelkerke 0.128 0.130 0.132
  Best model Selected model Inclusive model (2021)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 2.00 0.04 1.91 – 2.08 <0.001 2.00 0.04 1.91 – 2.08 <0.001 2.00 0.04 1.92 – 2.08 <0.001
K -0.08 0.05 -0.18 – 0.02 0.100 -0.10 0.06 -0.22 – 0.03 0.132 -0.09 0.06 -0.22 – 0.03 0.154
N -0.09 0.05 -0.19 – 0.01 0.073 -0.10 0.06 -0.23 – 0.02 0.105 -0.10 0.06 -0.23 – 0.02 0.098
P 0.11 0.04 0.03 – 0.19 0.009 0.11 0.04 0.03 – 0.19 0.009 0.12 0.04 0.03 – 0.20 0.009
C 0.02 0.06 -0.10 – 0.13 0.753 0.04 0.08 -0.12 – 0.19 0.598
CEC -0.03 0.07 -0.16 – 0.10 0.664
Observations 86 86 86
R2 Nagelkerke 0.128 0.130 0.132
  Best model Selected inclusive model (2020)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 1.52 0.03 1.46 – 1.58 <0.001 1.52 0.03 1.46 – 1.58 <0.001
P 0.06 0.03 -0.00 – 0.12 0.054 0.06 0.04 -0.02 – 0.13 0.153
C 0.07 0.03 0.01 – 0.13 0.034 0.10 0.05 -0.00 – 0.20 0.049
N 0.01 0.04 -0.07 – 0.10 0.732
K 0.07 0.05 -0.03 – 0.17 0.161
CEC -0.09 0.05 -0.20 – 0.01 0.078
Observations 221 221
R2 Nagelkerke 0.056 0.080
  Best model Selected inclusive model (2020)
Predictors Log-Mean std. Error CI p Log-Mean std. Error CI p
(Intercept) 1.52 0.03 1.46 – 1.58 <0.001 1.52 0.03 1.46 – 1.58 <0.001
P 0.06 0.03 -0.00 – 0.12 0.054 0.06 0.04 -0.02 – 0.13 0.153
C 0.07 0.03 0.01 – 0.13 0.034 0.10 0.05 -0.00 – 0.20 0.049
N 0.01 0.04 -0.07 – 0.10 0.732
K 0.07 0.05 -0.03 – 0.17 0.161
CEC -0.09 0.05 -0.20 – 0.01 0.078
Observations 221 221
R2 Nagelkerke 0.056 0.080
Diversity ~ nutrient composition
2020-2022: zero model
  Best model Selected model Inclusive model (all year)
Predictors Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p
(Intercept) 7.18 3.20 – 16.11 <0.001 7.16 3.20 – 16.03 <0.001 7.28 3.23 – 16.43 <0.001
N 1.57 1.13 – 2.19 0.007 1.61 1.09 – 2.37 0.016 1.62 1.09 – 2.39 0.016
P 0.65 0.49 – 0.86 0.003 0.64 0.48 – 0.86 0.003 0.67 0.50 – 0.91 0.010
K 1.61 1.12 – 2.33 0.011 1.65 1.06 – 2.59 0.028 1.78 1.09 – 2.91 0.020
C 0.96 0.67 – 1.40 0.846 1.13 0.70 – 1.83 0.628
CEC 0.78 0.50 – 1.23 0.286
Random Effects
σ2 3.29 3.29 3.29
τ00 0.39 time 0.39 time 0.40 time
ICC 0.11 0.11 0.11
N 3 time 3 time 3 time
Observations 393 393 393
Marginal R2 / Conditional R2 0.072 / 0.171 0.072 / 0.170 0.078 / 0.178
2020-2022: zero model
  Best model Selected model Inclusive model (all year)
Predictors Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p
(Intercept) 7.18 3.20 – 16.11 <0.001 7.16 3.20 – 16.03 <0.001 7.28 3.23 – 16.43 <0.001
N 1.57 1.13 – 2.19 0.007 1.61 1.09 – 2.37 0.016 1.62 1.09 – 2.39 0.016
P 0.65 0.49 – 0.86 0.003 0.64 0.48 – 0.86 0.003 0.67 0.50 – 0.91 0.010
K 1.61 1.12 – 2.33 0.011 1.65 1.06 – 2.59 0.028 1.78 1.09 – 2.91 0.020
C 0.96 0.67 – 1.40 0.846 1.13 0.70 – 1.83 0.628
CEC 0.78 0.50 – 1.23 0.286
Random Effects
σ2 3.29 3.29 3.29
τ00 0.39 time 0.39 time 0.40 time
ICC 0.11 0.11 0.11
N 3 time 3 time 3 time
Observations 393 393 393
Marginal R2 / Conditional R2 0.072 / 0.171 0.072 / 0.170 0.078 / 0.178
2020-2022: when Shannon diversity index > 0
  Best model Selected model Inclusive model (all year)
Predictors Estimates std. Error CI p Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 0.89 0.09 0.71 – 1.07 <0.001 0.89 0.09 0.71 – 1.07 <0.001 1.03 0.04 0.95 – 1.11 <0.001
N 0.09 0.03 0.03 – 0.14 0.002 0.09 0.04 0.02 – 0.16 0.016 0.02 0.03 -0.03 – 0.08 0.424
P -0.05 0.03 -0.11 – 0.01 0.102 -0.05 0.03 -0.11 – 0.01 0.105 0.04 0.03 -0.01 – 0.10 0.097
K 0.09 0.03 0.03 – 0.15 0.005 0.09 0.04 0.01 – 0.17 0.023 0.02 0.03 -0.04 – 0.09 0.457
C 0.00 0.04 -0.07 – 0.07 0.980 0.03 0.04 -0.05 – 0.10 0.484
CEC -0.03 0.03 -0.09 – 0.03 0.359
Random Effects
σ2 0.24 0.24 0.12
τ00 0.02 time 0.02 time 0.00 time
ICC 0.09 0.09 0.03
N 3 time 3 time 3 time
Observations 393 393 319
Marginal R2 / Conditional R2 0.031 / 0.114 0.031 / 0.114 0.023 / 0.049
2020-2022: when Shannon diversity index > 0
  Best model Selected model Inclusive model (all year)
Predictors Estimates std. Error CI p Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 0.89 0.09 0.71 – 1.07 <0.001 0.89 0.09 0.71 – 1.07 <0.001 1.03 0.04 0.95 – 1.11 <0.001
N 0.09 0.03 0.03 – 0.14 0.002 0.09 0.04 0.02 – 0.16 0.016 0.02 0.03 -0.03 – 0.08 0.424
P -0.05 0.03 -0.11 – 0.01 0.102 -0.05 0.03 -0.11 – 0.01 0.105 0.04 0.03 -0.01 – 0.10 0.097
K 0.09 0.03 0.03 – 0.15 0.005 0.09 0.04 0.01 – 0.17 0.023 0.02 0.03 -0.04 – 0.09 0.457
C 0.00 0.04 -0.07 – 0.07 0.980 0.03 0.04 -0.05 – 0.10 0.484
CEC -0.03 0.03 -0.09 – 0.03 0.359
Random Effects
σ2 0.24 0.24 0.12
τ00 0.02 time 0.02 time 0.00 time
ICC 0.09 0.09 0.03
N 3 time 3 time 3 time
Observations 393 393 319
Marginal R2 / Conditional R2 0.031 / 0.114 0.031 / 0.114 0.023 / 0.049
2022: zero model
  Best model Selected inclusive model
Predictors Log-Odds std. Error CI p Log-Odds std. Error CI p
(Intercept) 2.60 0.47 1.80 – 3.68 <0.001 9.08 5.39 2.95 – 26.37 0.092
P -0.81 0.28 -1.39 – -0.27 0.004 -2.71 1.58 -6.88 – -0.47 0.087
N 4.74 3.62 -0.43 – 14.53 0.190
K 10.80 7.92 0.84 – 33.02 0.172
C 5.15 5.09 -0.31 – 24.57 0.311
CEC -7.62 6.03 -28.15 – -0.36 0.207
Observations 84 84
R2 Tjur 0.161 0.236
2022: zero model
  Best model Selected inclusive model
Predictors Log-Odds std. Error CI p Log-Odds std. Error CI p
(Intercept) 2.60 0.47 1.80 – 3.68 <0.001 9.08 5.39 2.95 – 26.37 0.092
P -0.81 0.28 -1.39 – -0.27 0.004 -2.71 1.58 -6.88 – -0.47 0.087
N 4.74 3.62 -0.43 – 14.53 0.190
K 10.80 7.92 0.84 – 33.02 0.172
C 5.15 5.09 -0.31 – 24.57 0.311
CEC -7.62 6.03 -28.15 – -0.36 0.207
Observations 84 84
R2 Tjur 0.161 0.236
2022: when Shannon diversity index > 0
  Best model Selected inclusive model
Predictors Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 1.07 0.04 0.98 – 1.15 <0.001 1.09 0.05 1.00 – 1.18 <0.001
P 0.05 0.05 -0.04 – 0.15 0.278 0.02 0.06 -0.11 – 0.14 0.788
N 0.06 0.07 -0.08 – 0.19 0.414
K 0.06 0.07 -0.07 – 0.20 0.347
C 0.09 0.08 -0.08 – 0.25 0.303
CEC -0.04 0.07 -0.18 – 0.11 0.605
Observations 76 76
R2 / R2 adjusted 0.016 / 0.003 0.087 / 0.022
2022: when Shannon diversity index > 0
  Best model Selected inclusive model
Predictors Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 1.07 0.04 0.98 – 1.15 <0.001 1.09 0.05 1.00 – 1.18 <0.001
P 0.05 0.05 -0.04 – 0.15 0.278 0.02 0.06 -0.11 – 0.14 0.788
N 0.06 0.07 -0.08 – 0.19 0.414
K 0.06 0.07 -0.07 – 0.20 0.347
C 0.09 0.08 -0.08 – 0.25 0.303
CEC -0.04 0.07 -0.18 – 0.11 0.605
Observations 76 76
R2 / R2 adjusted 0.016 / 0.003 0.087 / 0.022
2021: zero model
  Best model Selected inclusive model
Predictors Log-Odds std. Error CI p Log-Odds std. Error CI p
(Intercept) 8.30 10.98 2.70 – NA 0.450 46.61 6288.84 -241.78 – 2010.82 0.994
P -5.06 7.69 NA – -1.03 0.510 -29.85 4183.23 NA – 334.61 0.994
K -1.56 2.22 NA – 0.06 0.481 -3.50 643.05 NA – 52.53 0.996
C -3.79 5.90 NA – 0.38 0.520 -26.77 3623.33 NA – 288.90 0.994
CEC 9.74 15.71 1.52 – NA 0.535 58.80 8374.73 -670.82 – NA 0.994
N 12.25 1310.41 -91.01 – NA 0.993
Observations 86 86
R2 Tjur 0.187 0.190
2021: zero model
  Best model Selected inclusive model
Predictors Log-Odds std. Error CI p Log-Odds std. Error CI p
(Intercept) 8.30 10.98 2.70 – NA 0.450 46.61 6288.84 -241.78 – 2010.82 0.994
P -5.06 7.69 NA – -1.03 0.510 -29.85 4183.23 NA – 334.61 0.994
K -1.56 2.22 NA – 0.06 0.481 -3.50 643.05 NA – 52.53 0.996
C -3.79 5.90 NA – 0.38 0.520 -26.77 3623.33 NA – 288.90 0.994
CEC 9.74 15.71 1.52 – NA 0.535 58.80 8374.73 -670.82 – NA 0.994
N 12.25 1310.41 -91.01 – NA 0.993
Observations 86 86
R2 Tjur 0.187 0.190
2021: when Shannon diversity index > 0
  Best model Selected inclusive model
Predictors Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 1.06 0.04 0.99 – 1.14 <0.001 1.06 0.04 0.98 – 1.14 <0.001
P 0.11 0.05 0.01 – 0.21 0.025 0.12 0.05 0.02 – 0.22 0.021
K -0.01 0.05 -0.11 – 0.09 0.864 -0.04 0.06 -0.16 – 0.09 0.548
C -0.04 0.06 -0.17 – 0.09 0.565 -0.00 0.08 -0.16 – 0.15 0.973
CEC -0.04 0.06 -0.17 – 0.08 0.506 -0.05 0.06 -0.17 – 0.08 0.451
N -0.05 0.06 -0.16 – 0.07 0.413
Observations 78 78
R2 / R2 adjusted 0.103 / 0.053 0.111 / 0.049
2021: when Shannon diversity index > 0
  Best model Selected inclusive model
Predictors Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 1.06 0.04 0.99 – 1.14 <0.001 1.06 0.04 0.98 – 1.14 <0.001
P 0.11 0.05 0.01 – 0.21 0.025 0.12 0.05 0.02 – 0.22 0.021
K -0.01 0.05 -0.11 – 0.09 0.864 -0.04 0.06 -0.16 – 0.09 0.548
C -0.04 0.06 -0.17 – 0.09 0.565 -0.00 0.08 -0.16 – 0.15 0.973
CEC -0.04 0.06 -0.17 – 0.08 0.506 -0.05 0.06 -0.17 – 0.08 0.451
N -0.05 0.06 -0.16 – 0.07 0.413
Observations 78 78
R2 / R2 adjusted 0.103 / 0.053 0.111 / 0.049
2020: zero model
  Best model Selected inclusive model
Predictors Log-Odds std. Error CI p Log-Odds std. Error CI p
(Intercept) 8.30 10.98 2.70 – NA 0.450 1.13 0.16 0.81 – 1.46 <0.001
P -5.06 7.69 NA – -1.03 0.510 -0.15 0.20 -0.54 – 0.24 0.441
K -1.56 2.22 NA – 0.06 0.481 0.75 0.32 0.18 – 1.44 0.017
C -3.79 5.90 NA – 0.38 0.520 0.08 0.27 -0.44 – 0.64 0.771
CEC 9.74 15.71 1.52 – NA 0.535 -0.42 0.27 -0.95 – 0.10 0.119
N 0.54 0.22 0.11 – 0.98 0.014
Observations 86 223
R2 Tjur 0.187 0.049
2020: zero model
  Best model Selected inclusive model
Predictors Log-Odds std. Error CI p Log-Odds std. Error CI p
(Intercept) 8.30 10.98 2.70 – NA 0.450 1.13 0.16 0.81 – 1.46 <0.001
P -5.06 7.69 NA – -1.03 0.510 -0.15 0.20 -0.54 – 0.24 0.441
K -1.56 2.22 NA – 0.06 0.481 0.75 0.32 0.18 – 1.44 0.017
C -3.79 5.90 NA – 0.38 0.520 0.08 0.27 -0.44 – 0.64 0.771
CEC 9.74 15.71 1.52 – NA 0.535 -0.42 0.27 -0.95 – 0.10 0.119
N 0.54 0.22 0.11 – 0.98 0.014
Observations 86 223
R2 Tjur 0.187 0.049
2020: when Shannon diversity index > 0
  Best model Selected inclusive model
Predictors Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 1.06 0.04 0.99 – 1.14 <0.001 0.96 0.03 0.91 – 1.01 <0.001
P 0.11 0.05 0.01 – 0.21 0.025 0.02 0.04 -0.05 – 0.09 0.577
K -0.01 0.05 -0.11 – 0.09 0.864 0.03 0.04 -0.05 – 0.12 0.433
C -0.04 0.06 -0.17 – 0.09 0.565 0.02 0.05 -0.08 – 0.11 0.741
CEC -0.04 0.06 -0.17 – 0.08 0.506 -0.02 0.05 -0.11 – 0.07 0.645
N 0.04 0.04 -0.03 – 0.12 0.269
Observations 78 165
R2 / R2 adjusted 0.103 / 0.053 0.022 / -0.009
2020: when Shannon diversity index > 0
  Best model Selected inclusive model
Predictors Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 1.06 0.04 0.99 – 1.14 <0.001 0.96 0.03 0.91 – 1.01 <0.001
P 0.11 0.05 0.01 – 0.21 0.025 0.02 0.04 -0.05 – 0.09 0.577
K -0.01 0.05 -0.11 – 0.09 0.864 0.03 0.04 -0.05 – 0.12 0.433
C -0.04 0.06 -0.17 – 0.09 0.565 0.02 0.05 -0.08 – 0.11 0.741
CEC -0.04 0.06 -0.17 – 0.08 0.506 -0.02 0.05 -0.11 – 0.07 0.645
N 0.04 0.04 -0.03 – 0.12 0.269
Observations 78 165
R2 / R2 adjusted 0.103 / 0.053 0.022 / -0.009
Composition ~ nutrient composition

2020 - There is no significant correlation between nutrient elements and plant composition.

## Warning in matrify(commsub): NAs introduced by coercion
## 
## ***VECTORS
## 
##        NMDS1    NMDS2     r2 Pr(>r)
## N   -0.69572 -0.71832 0.1044  0.526
## P    1.00000 -0.00228 0.2460  0.223
## K    0.36671  0.93034 0.1746  0.351
## C   -0.83019  0.55748 0.1548  0.398
## pH   0.88220  0.47088 0.3441  0.115
## CEC  0.11270  0.99363 0.1267  0.511
## Permutation: free
## Number of permutations: 999

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

2021 - There is no significant correlation between nutrient elements and plant composition.

## Warning in matrify(commsub): NAs introduced by coercion
## [1] 14
## Run 0 stress 0.07021466 
## Run 1 stress 0.07630436 
## Run 2 stress 0.07647517 
## Run 3 stress 0.07021458 
## ... New best solution
## ... Procrustes: rmse 0.0001369898  max resid 0.0002663469 
## ... Similar to previous best
## Run 4 stress 0.07647552 
## Run 5 stress 0.07630445 
## Run 6 stress 0.07697591 
## Run 7 stress 0.07021458 
## ... Procrustes: rmse 2.093676e-05  max resid 3.787053e-05 
## ... Similar to previous best
## Run 8 stress 0.07647601 
## Run 9 stress 0.07697588 
## Run 10 stress 0.07021473 
## ... Procrustes: rmse 0.0002414905  max resid 0.0004425255 
## ... Similar to previous best
## Run 11 stress 0.07021468 
## ... Procrustes: rmse 0.0002164358  max resid 0.0004019018 
## ... Similar to previous best
## Run 12 stress 0.07697604 
## Run 13 stress 0.07697596 
## Run 14 stress 0.0770589 
## Run 15 stress 0.0702147 
## ... Procrustes: rmse 0.0002379547  max resid 0.0004413267 
## ... Similar to previous best
## Run 16 stress 0.08006072 
## Run 17 stress 0.07630436 
## Run 18 stress 0.07697595 
## Run 19 stress 0.08006118 
## Run 20 stress 0.07647477 
## *** Best solution repeated 5 times
## 
## ***VECTORS
## 
##        NMDS1    NMDS2     r2 Pr(>r)  
## N    0.44546 -0.89530 0.3457  0.099 .
## P   -0.86865  0.49542 0.1385  0.443  
## K   -0.75126  0.66001 0.1268  0.454  
## C    0.84944 -0.52768 0.0169  0.921  
## pH  -0.92571 -0.37823 0.3009  0.146  
## CEC -0.88036 -0.47431 0.0770  0.644  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999

2022 - There is no significant correlation between nutrient elements and plant composition.

## [1] 87
##  [1] "acul"  "aicu"  "arca"  "ardy"  "arsp"  "auel"  "bldr"  "blrd"  "brdi" 
## [10] "brdr"  "brpe"  "brru"  "buse"  "caer"  "cagr"  "cahi"  "casp"  "cear" 
## [19] "chau"  "chei"  "chps"  "crcl"  "crco"  "cusc"  "cusp"  "dagl"  "dosp" 
## [28] "ento"  "erau"  "ercy"  "erra"  "ersp"  "gite"  "gnte"  "gobe"  "gocy" 
## [37] "gono"  "goro"  "gosp"  "haod"  "hygl"  "hypi"  "hypo"  "jubu"  "laro" 
## [46] "ledu"  "lele"  "loef"  "misp"  "mite"  "momo"  "mopa"  "niro"  "omco" 
## [55] "orsp"  "pala"  "peai"  "pedu"  "phsu"  "plde"  "poar"  "poca"  "pocap"
## [64] "poce"  "pogn"  "pole"  "pomu"  "pter"  "ptga"  "ptob"  "rhla"  "rhpy" 
## [73] "rhsp"  "ry"    "scna"  "sino"  "sool"  "stfi"  "stpi"  "thma"  "trcy" 
## [82] "tris"  "tror"  "trpi"  "waac"  "wagr"  "x"     "grps"
##  [1] "acul"      "aicu"      "arca"      "ardy"      "arsp"      "auel"     
##  [7] "bldr"      "blrd"      "brdi"      "brdr"      "brpe"      "brru"     
## [13] "buse"      "caer"      "cagr"      "cahi"      "casp"      "cear"     
## [19] "chau"      "chei"      "chps"      "crcl"      "crco"      "cusc"     
## [25] "cusp"      "dagl"      "dosp"      "ento"      "erau"      "ercy"     
## [31] "erra"      "ersp"      "gite"      "gnte"      "gobe"      "gocy"     
## [37] "gono"      "goro"      "gosp"      "haod"      "hygl"      "hypi"     
## [43] "hypo"      "jubu"      "laro"      "ledu"      "lele"      "loef"     
## [49] "misp"      "mite"      "momo"      "mopa"      "niro"      "omco"     
## [55] "orsp"      "pala"      "peai"      "pedu"      "phsu"      "plde"     
## [61] "poar"      "poca"      "pocap"     "poce"      "pogn"      "pole"     
## [67] "pomu"      "pter"      "ptga"      "ptob"      "rhla"      "rhpy"     
## [73] "rhsp"      "ry"        "scna"      "sino"      "sool"      "stfi"     
## [79] "stpi"      "thma"      "trcy"      "tris"      "tror"      "trpi"     
## [85] "waac"      "wagr"      "x"         "time"      "block"     "transect" 
## [91] "init"      "treatment"
## 
## ***VECTORS
## 
##        NMDS1    NMDS2     r2 Pr(>r)  
## N   -0.64109  0.76747 0.0367  0.829  
## P    0.95871 -0.28438 0.2378  0.224  
## K    0.61485  0.78864 0.0906  0.585  
## C   -0.02094  0.99978 0.3652  0.088 .
## pH   0.94758 -0.31952 0.2138  0.278  
## CEC  0.33540  0.94208 0.1705  0.363  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999

2020-2022 - P and pH seem to be significantly affecting the compositions of log and open communities - I doubt it since envfit function do not take ‘year’ as a random effect.

## [1] 42
## 
## ***VECTORS
## 
##        NMDS1    NMDS2     r2 Pr(>r)   
## N    0.63084 -0.77592 0.0902  0.143   
## P   -0.52710  0.84981 0.2411  0.005 **
## K   -0.45729  0.88932 0.0783  0.206   
## C    0.63344 -0.77379 0.0728  0.248   
## pH  -0.72398  0.68982 0.2342  0.004 **
## CEC -0.85930  0.51148 0.0032  0.926   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999

Interpretation

Q3 Are/how are plant species performances affected by proximity to fallen logs?

Overview of results

2021

There are three response variables: presence (binomial), count (truncated poisson), and per capita biomass (gaussian).

Presence - The best fit model for presence is a tie between between physical barrier and the additive model of physical barrier + nutrient island (deltaAIC=0.32). I present the model analysis of the additive model. - Both TRCY and TROR are significantly more likely to be present where there is a physical barrier as compared to in the open (TRCY: p=0.03, TROR: p=0.01) - Nutrient island does not significantly explain variation in presence/absence in any species. However, all species trend to having higher probability of presence in log-legacy plots.

Count - The best fit model for count is nutrient island. - Nutrient island does not strongly significantly explain variation in presence/absence in any species. TRCY is marginally significantly affected by nutrient island, where there are more TRCY in plots where there is a `log legacy’ (there had been a decomposing log in the plot before the experiment began)

Biomass - The best fit model for per capita biomass is the nutrient island, then physical barrier island, then the additive model. They are all within 2 AICc points of each other (deltaAIC=1.55 and 1.76, respectively). I present the model analysis of the additive model. - Nutrient island significantly explains variation GORO biomass, where there are larger GORO plants in plots where there is a `log legacy’ (p=0.03). - Physical barrier does not explain variation in biomass for any species.

2022

There are again three response variables: presence (binomial), count (truncated poisson), and per capita biomass (gaussian).

Presence - The best fit model for presence is a tie between between physical barrier and the additive model of physical barrier + nutrient island (deltaAIC=0.24). I present the model analysis of the additive model. - Both TRCY and TROR are significantly more likely to be present where there is a physical barrier as compared to in the open (TRCY: p=0.007, TROR: p=0.03) - Nutrient island significantly explains the presence/absence of TRCY, where TRCY is more likely to be present in log legacy plots as compared to open legacy plots (p=0.02)

Count - The best fit model for count is a tie between nutrient island and physical barrier. I present the model analysis of the nutrient island model. - Nutrient island does not strongly significantly explain variation in presence/absence in any species.

Biomass - The best fit model for per capita biomass is the nutrient island, then the additive model, then the physical barrier model. They are all within 1 AICc points of each other (deltaAIC=0.31 and 0.46, respectively). I present the model analysis of the additive model. - Nutrient island significantly explains variation in TROR biomass, where log legacy plots have larger TROR as compared to open legacy plots (p=0.04) - Physical barrier significantly explains variation in TROR biomass, where plots in the open have larger TROR than plots near a physical barrier (either log or PVC pipe) (p=0.02)

Statistical Methods

The basic approach is to analyse count and biomass data from 2021 and 2022 sowing experiment. We sowed 15 seeds for each species into 16 plots in each of the seven blocks. There are 6 plot type treatments. The gap and open treatments each have four replicates per block, and the insitu_log, insitu_pvc, open_with_log, and open_with_pvc each have two replicates per block. note: For each treatment of plot type, there is also a dispersal treatment, but I do not analyse that here (yet).
- For count data I use a glmmTMB to run a generalized linear mixed effects model approach to analyse the data. The hurdle model approach I am using is to first code presence/absence as 0 or 1 (1 being any nonzero count value) and run this analysis as a binomial regression. Depending on the model fit and residual dispersion (using DHARMa), I then either run a truncated poisson or truncated negative binomial regression on the count data. Though I did run alternative versions of hurdle models that predict count while accounting for zero inflation, I felt that analyzing the presence/absence, and then analyzing count, may be revealing more of the biology of the system, where presence/absence was distinctly affected by treatments while count was less so.
- For biomass data I use linear mixed effects model to analyse the per capita biomass data. I first do a log(1+n) transformation on the per capita biomass, then analyze. I chose to do per capita biomass because of recalcitrant (!!) residual dispersion in total biomass, even after attemtps at log and square root transformations.
I chose to do a model selection approach, which I’m trying to move away from generally. However, the models I use to analyze the data represent different hypotheses about why plant species might perform differently, and so I chose to do a model comparison and selection approach as a way to not only understand how plant species performance is affected by proximity to fallen logs, but also gain insight as to why this may be the case.

N.B.:Model comparison might not be the approach we want, and we may want to run the analysis differently. I’m open to discussion and change on this point.

After model comparison, I use estimated marginal means (package emmeans) on the best fit model and compute the significance of the difference in estimates.

The models I use test the hypotheses from above: Log decomposition creates islands of fertility directly around the fallen log and fallen logs alter the microclimate directly around them by providing shade.

Analysis

2021
### doing vital rate analysis

# packages 
require(lme4)
require(emmeans)
require(pscl)
require(glmmTMB)
require(tidyr)
require(DHARMa)
require(ggplot2)
require(AICcmodavg)
require(ggpubr)

############## 2021 data #############

# read csv
dat<-read.csv('nplants_data_2021_git.csv', header=T)
dat1<-dat[which(dat$seeding_trt==1),]
dat1$physical_barrier<-as.factor(dat1$physical_barrier)
dat1$block<-as.factor(dat1$block)

############# treatment response: do analysis for count by species ############# 

dat2<-(dat1[c(1:4,7,11,15,22)]) # these are block, transect, initial, current_plot_type, ngoro, ntrcy, ntror, physical_barrier
head(dat2) 
##   block transect initial current_plot_type ngoro ntrcy ntror physical_barrier
## 1     1     1.01     log               gap     3     0     0                0
## 2     1     1.02     log        insitu_log     0     0     0                1
## 3     1     1.03     log        insitu_pvc     1     3     3                1
## 4     1     1.14     log        insitu_log     0     0     0                1
## 5     1     1.17    open              open     0     0     0                0
## 6     1     1.19    open              open     2     0     0                0
countdat<-as.data.frame(dat2 %>% pivot_longer(c(ntror, ngoro, ntrcy)))
countdat$value<-as.numeric(ifelse(countdat$value>15, 15, countdat$value))
countmod<-glmmTMB(value~name*current_plot_type+(1|block), family="poisson", data=countdat)

# test for fit and zero inflation
 sim<-simulateResiduals(countmod)
 testZeroInflation(sim) # zero-inflated so need something else 

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.5731, p-value < 2.2e-16
## alternative hypothesis: two.sided
 plot(sim)

## going to do a hurdle model, which assumes a zero is only generated in one way 
# https://jsdajournal.springeropen.com/articles/10.1186/s40488-021-00121-4
# #https://stats.stackexchange.com/questions/81457/what-is-the-difference-between-zero-inflated-and-hurdle-models

# hurdle model 
# using examples as presented here: https://www.biorxiv.org/content/biorxiv/suppl/2017/05/01/132753.DC1/132753-2.pdf 
fit3<-glmmTMB(value~name*current_plot_type+(1|block), ziformula=~., family=nbinom2(), data=countdat)
 sim3<-simulateResiduals(fit3)
 plot(sim3)

 testDispersion(sim3) # looks nice ! 

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.99071, p-value = 0.896
## alternative hypothesis: two.sided
 testZeroInflation(sim3) # looks nice ! 

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 0.99276, p-value = 0.912
## alternative hypothesis: two.sided
summary(fit3)
##  Family: nbinom2  ( log )
## Formula:          value ~ name * current_plot_type + (1 | block)
## Zero inflation:         ~.
## Data: countdat
## 
##      AIC      BIC   logLik deviance df.resid 
##   1101.2   1250.1   -511.6   1023.2      297 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.03237  0.1799  
## Number of obs: 336, groups:  block, 7
## 
## Zero-inflation model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.9243   0.9614  
## Number of obs: 336, groups:  block, 7
## 
## Dispersion parameter for nbinom2 family (): 1.68 
## 
## Conditional model:
##                                          Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                               0.42749    0.31299   1.366   0.1720  
## namentrcy                                 0.82532    0.39283   2.101   0.0356 *
## namentror                                -0.12276    0.44057  -0.279   0.7805  
## current_plot_typeinsitu_log              -1.07022    0.53312  -2.007   0.0447 *
## current_plot_typeinsitu_pvc              -0.02408    0.44823  -0.054   0.9572  
## current_plot_typeopen                    -0.05040    0.40107  -0.126   0.9000  
## current_plot_typeopen_with_log           -0.24213    0.50393  -0.480   0.6309  
## current_plot_typeopen_with_pvc            0.35404    0.40916   0.865   0.3869  
## namentrcy:current_plot_typeinsitu_log     1.05024    0.67754   1.550   0.1211  
## namentror:current_plot_typeinsitu_log     0.83983    0.81808   1.027   0.3046  
## namentrcy:current_plot_typeinsitu_pvc    -0.20308    0.59085  -0.344   0.7311  
## namentror:current_plot_typeinsitu_pvc     0.61733    0.63892   0.966   0.3339  
## namentrcy:current_plot_typeopen          -0.51151    0.60272  -0.849   0.3961  
## namentror:current_plot_typeopen           0.50564    0.70479   0.717   0.4731  
## namentrcy:current_plot_typeopen_with_log -0.05338    0.70024  -0.076   0.9392  
## namentror:current_plot_typeopen_with_log  0.31312    0.75666   0.414   0.6790  
## namentrcy:current_plot_typeopen_with_pvc -1.04638    0.57985  -1.805   0.0711 .
## namentror:current_plot_typeopen_with_pvc -0.33034    0.59667  -0.554   0.5798  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##                                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                              -1.396e+00  1.135e+00  -1.230    0.219
## namentrcy                                 9.858e-01  1.131e+00   0.872    0.383
## namentror                                 4.034e-01  1.305e+00   0.309    0.757
## current_plot_typeinsitu_log              -1.628e+01  5.111e+03  -0.003    0.997
## current_plot_typeinsitu_pvc              -2.684e+00  7.986e+00  -0.336    0.737
## current_plot_typeopen                    -1.280e+00  2.202e+00  -0.582    0.561
## current_plot_typeopen_with_log           -9.202e-01  2.380e+00  -0.387    0.699
## current_plot_typeopen_with_pvc           -1.790e+01  4.876e+03  -0.004    0.997
## namentrcy:current_plot_typeinsitu_log     1.551e+01  5.111e+03   0.003    0.998
## namentror:current_plot_typeinsitu_log     1.661e+01  5.111e+03   0.003    0.997
## namentrcy:current_plot_typeinsitu_pvc    -5.449e-01  8.249e+00  -0.066    0.947
## namentror:current_plot_typeinsitu_pvc     1.947e+00  8.039e+00   0.242    0.809
## namentrcy:current_plot_typeopen           1.665e+00  2.423e+00   0.687    0.492
## namentror:current_plot_typeopen           3.564e+00  2.581e+00   1.381    0.167
## namentrcy:current_plot_typeopen_with_log  5.747e-01  2.630e+00   0.218    0.827
## namentror:current_plot_typeopen_with_log  8.341e-01  2.920e+00   0.286    0.775
## namentrcy:current_plot_typeopen_with_pvc  1.553e+01  4.876e+03   0.003    0.997
## namentror:current_plot_typeopen_with_pvc -9.760e+00  5.916e+05   0.000    1.000
emmip(fit3,~current_plot_type|name, type='response',CI=T)

# visualize
est<-emmeans(fit3,~current_plot_type|name, type='response')
pairs(est)
## name = ngoro:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              2.916 1.555 Inf    1   2.007  0.3379
##  gap / insitu_pvc              1.024 0.459 Inf    1   0.054  1.0000
##  gap / open                    1.052 0.422 Inf    1   0.126  1.0000
##  gap / open_with_log           1.274 0.642 Inf    1   0.480  0.9968
##  gap / open_with_pvc           0.702 0.287 Inf    1  -0.865  0.9547
##  insitu_log / insitu_pvc       0.351 0.198 Inf    1  -1.855  0.4301
##  insitu_log / open             0.361 0.189 Inf    1  -1.943  0.3759
##  insitu_log / open_with_log    0.437 0.261 Inf    1  -1.385  0.7365
##  insitu_log / open_with_pvc    0.241 0.123 Inf    1  -2.777  0.0611
##  insitu_pvc / open             1.027 0.443 Inf    1   0.061  1.0000
##  insitu_pvc / open_with_log    1.244 0.667 Inf    1   0.407  0.9986
##  insitu_pvc / open_with_pvc    0.685 0.308 Inf    1  -0.842  0.9596
##  open / open_with_log          1.211 0.588 Inf    1   0.395  0.9988
##  open / open_with_pvc          0.667 0.266 Inf    1  -1.015  0.9131
##  open_with_log / open_with_pvc 0.551 0.270 Inf    1  -1.215  0.8297
## 
## name = ntrcy:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              1.020 0.442 Inf    1   0.046  1.0000
##  gap / insitu_pvc              1.255 0.483 Inf    1   0.590  0.9917
##  gap / open                    1.754 0.770 Inf    1   1.280  0.7961
##  gap / open_with_log           1.344 0.642 Inf    1   0.619  0.9897
##  gap / open_with_pvc           1.998 0.840 Inf    1   1.648  0.5668
##  insitu_log / insitu_pvc       1.230 0.545 Inf    1   0.467  0.9972
##  insitu_log / open             1.719 0.827 Inf    1   1.127  0.8704
##  insitu_log / open_with_log    1.317 0.721 Inf    1   0.503  0.9961
##  insitu_log / open_with_pvc    1.959 0.928 Inf    1   1.420  0.7149
##  insitu_pvc / open             1.398 0.619 Inf    1   0.756  0.9747
##  insitu_pvc / open_with_log    1.071 0.531 Inf    1   0.138  1.0000
##  insitu_pvc / open_with_pvc    1.592 0.684 Inf    1   1.083  0.8885
##  open / open_with_log          0.766 0.421 Inf    1  -0.485  0.9967
##  open / open_with_pvc          1.139 0.543 Inf    1   0.273  0.9998
##  open_with_log / open_with_pvc 1.487 0.784 Inf    1   0.752  0.9752
## 
## name = ntror:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              1.259 0.788 Inf    1   0.368  0.9991
##  gap / insitu_pvc              0.553 0.255 Inf    1  -1.283  0.7944
##  gap / open                    0.634 0.369 Inf    1  -0.783  0.9704
##  gap / open_with_log           0.931 0.520 Inf    1  -0.127  1.0000
##  gap / open_with_pvc           0.977 0.441 Inf    1  -0.053  1.0000
##  insitu_log / insitu_pvc       0.439 0.274 Inf    1  -1.321  0.7735
##  insitu_log / open             0.504 0.362 Inf    1  -0.954  0.9323
##  insitu_log / open_with_log    0.740 0.520 Inf    1  -0.429  0.9982
##  insitu_log / open_with_pvc    0.776 0.477 Inf    1  -0.413  0.9985
##  insitu_pvc / open             1.148 0.666 Inf    1   0.238  0.9999
##  insitu_pvc / open_with_log    1.686 0.948 Inf    1   0.929  0.9392
##  insitu_pvc / open_with_pvc    1.767 0.780 Inf    1   1.291  0.7900
##  open / open_with_log          1.469 0.975 Inf    1   0.579  0.9924
##  open / open_with_pvc          1.540 0.882 Inf    1   0.753  0.9751
##  open_with_log / open_with_pvc 1.048 0.575 Inf    1   0.086  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## Tests are performed on the log scale
#### now i'll do a by-hand hurdle model on my own using a truncated negative binomial

### zeros and ones 
countdat$presence<-ifelse(countdat$value==0, 0, 1)
zerofit<-glmmTMB(presence~name*current_plot_type+(1 | block), family=binomial, data=countdat, REML=FALSE)
emmip(zerofit,~current_plot_type|name, type='response',CI=T)

est<-emmeans(zerofit, ~current_plot_type|name, type='response')
pairs(est)
## name = ngoro:
##  contrast                      odds.ratio    SE  df null z.ratio p.value
##  gap / insitu_log                   1.175 0.808 Inf    1   0.235  0.9999
##  gap / insitu_pvc                   0.656 0.434 Inf    1  -0.638  0.9882
##  gap / open                         0.676 0.376 Inf    1  -0.705  0.9814
##  gap / open_with_log                0.741 0.499 Inf    1  -0.446  0.9978
##  gap / open_with_pvc                0.145 0.125 Inf    1  -2.246  0.2167
##  insitu_log / insitu_pvc            0.558 0.437 Inf    1  -0.745  0.9763
##  insitu_log / open                  0.575 0.400 Inf    1  -0.796  0.9684
##  insitu_log / open_with_log         0.630 0.500 Inf    1  -0.582  0.9922
##  insitu_log / open_with_pvc         0.123 0.118 Inf    1  -2.189  0.2430
##  insitu_pvc / open                  1.030 0.690 Inf    1   0.045  1.0000
##  insitu_pvc / open_with_log         1.130 0.870 Inf    1   0.159  1.0000
##  insitu_pvc / open_with_pvc         0.221 0.207 Inf    1  -1.610  0.5920
##  open / open_with_log               1.097 0.747 Inf    1   0.135  1.0000
##  open / open_with_pvc               0.215 0.186 Inf    1  -1.777  0.4805
##  open_with_log / open_with_pvc      0.196 0.185 Inf    1  -1.725  0.5148
## 
## name = ntrcy:
##  contrast                      odds.ratio    SE  df null z.ratio p.value
##  gap / insitu_log                   0.612 0.428 Inf    1  -0.702  0.9818
##  gap / insitu_pvc                   0.350 0.248 Inf    1  -1.483  0.6755
##  gap / open                         1.737 0.974 Inf    1   0.984  0.9231
##  gap / open_with_log                1.000 0.668 Inf    1   0.000  1.0000
##  gap / open_with_pvc                0.487 0.331 Inf    1  -1.059  0.8976
##  insitu_log / insitu_pvc            0.571 0.475 Inf    1  -0.673  0.9849
##  insitu_log / open                  2.837 2.017 Inf    1   1.467  0.6857
##  insitu_log / open_with_log         1.633 1.304 Inf    1   0.614  0.9900
##  insitu_log / open_with_pvc         0.796 0.642 Inf    1  -0.283  0.9998
##  insitu_pvc / open                  4.965 3.577 Inf    1   2.224  0.2265
##  insitu_pvc / open_with_log         2.858 2.306 Inf    1   1.302  0.7842
##  insitu_pvc / open_with_pvc         1.393 1.135 Inf    1   0.407  0.9986
##  open / open_with_log               0.576 0.392 Inf    1  -0.811  0.9656
##  open / open_with_pvc               0.281 0.194 Inf    1  -1.838  0.4411
##  open_with_log / open_with_pvc      0.487 0.381 Inf    1  -0.920  0.9415
## 
## name = ntror:
##  contrast                      odds.ratio    SE  df null z.ratio p.value
##  gap / insitu_log                   1.211 0.848 Inf    1   0.273  0.9998
##  gap / insitu_pvc                   0.360 0.245 Inf    1  -1.499  0.6651
##  gap / open                         3.440 2.188 Inf    1   1.942  0.3763
##  gap / open_with_log                1.000 0.675 Inf    1   0.000  1.0000
##  gap / open_with_pvc                0.487 0.323 Inf    1  -1.083  0.8882
##  insitu_log / insitu_pvc            0.298 0.240 Inf    1  -1.501  0.6637
##  insitu_log / open                  2.841 2.185 Inf    1   1.358  0.7523
##  insitu_log / open_with_log         0.826 0.662 Inf    1  -0.238  0.9999
##  insitu_log / open_with_pvc         0.402 0.319 Inf    1  -1.149  0.8608
##  insitu_pvc / open                  9.545 7.181 Inf    1   2.999  0.0324
##  insitu_pvc / open_with_log         2.775 2.178 Inf    1   1.301  0.7848
##  insitu_pvc / open_with_pvc         1.351 1.047 Inf    1   0.388  0.9989
##  open / open_with_log               0.291 0.217 Inf    1  -1.656  0.5612
##  open / open_with_pvc               0.142 0.104 Inf    1  -2.652  0.0851
##  open_with_log / open_with_pvc      0.487 0.375 Inf    1  -0.934  0.9377
## 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## Tests are performed on the log odds ratio scale
### abundance with a truncated negbinom 
countdat$posicounts<-as.numeric(ifelse(countdat$value==0, "NA", countdat$value))
countfit<-glmmTMB(posicounts~name*current_plot_type+(1 | block), family=truncated_nbinom2(), data=countdat, REML=FALSE)
emmip(countfit,~current_plot_type|name, type='response',CI=T)

est<-emmeans(countfit, ~current_plot_type|name, type='response')
pairs(est)
## name = ngoro:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              8.005 8.954 Inf    1   1.860  0.4273
##  gap / insitu_pvc              0.936 0.491 Inf    1  -0.126  1.0000
##  gap / open                    1.074 0.491 Inf    1   0.155  1.0000
##  gap / open_with_log           1.545 0.919 Inf    1   0.731  0.9781
##  gap / open_with_pvc           0.928 0.442 Inf    1  -0.157  1.0000
##  insitu_log / insitu_pvc       0.117 0.134 Inf    1  -1.877  0.4162
##  insitu_log / open             0.134 0.149 Inf    1  -1.804  0.4634
##  insitu_log / open_with_log    0.193 0.227 Inf    1  -1.399  0.7276
##  insitu_log / open_with_pvc    0.116 0.130 Inf    1  -1.923  0.3880
##  insitu_pvc / open             1.147 0.591 Inf    1   0.266  0.9998
##  insitu_pvc / open_with_log    1.650 1.056 Inf    1   0.783  0.9705
##  insitu_pvc / open_with_pvc    0.992 0.528 Inf    1  -0.016  1.0000
##  open / open_with_log          1.439 0.844 Inf    1   0.620  0.9896
##  open / open_with_pvc          0.864 0.403 Inf    1  -0.313  0.9996
##  open_with_log / open_with_pvc 0.601 0.360 Inf    1  -0.849  0.9582
## 
## name = ntrcy:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              1.029 0.484 Inf    1   0.061  1.0000
##  gap / insitu_pvc              1.144 0.497 Inf    1   0.310  0.9996
##  gap / open                    1.790 0.845 Inf    1   1.233  0.8207
##  gap / open_with_log           1.246 0.628 Inf    1   0.437  0.9980
##  gap / open_with_pvc           2.168 1.045 Inf    1   1.605  0.5953
##  insitu_log / insitu_pvc       1.112 0.549 Inf    1   0.214  0.9999
##  insitu_log / open             1.739 0.913 Inf    1   1.055  0.8991
##  insitu_log / open_with_log    1.211 0.685 Inf    1   0.339  0.9994
##  insitu_log / open_with_pvc    2.107 1.144 Inf    1   1.372  0.7437
##  insitu_pvc / open             1.565 0.768 Inf    1   0.912  0.9436
##  insitu_pvc / open_with_log    1.089 0.579 Inf    1   0.161  1.0000
##  insitu_pvc / open_with_pvc    1.895 0.965 Inf    1   1.255  0.8094
##  open / open_with_log          0.696 0.394 Inf    1  -0.639  0.9881
##  open / open_with_pvc          1.211 0.656 Inf    1   0.354  0.9993
##  open_with_log / open_with_pvc 1.739 0.992 Inf    1   0.970  0.9274
## 
## name = ntror:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              1.536 1.136 Inf    1   0.580  0.9923
##  gap / insitu_pvc              0.622 0.320 Inf    1  -0.924  0.9406
##  gap / open                    0.618 0.388 Inf    1  -0.766  0.9731
##  gap / open_with_log           0.843 0.519 Inf    1  -0.277  0.9998
##  gap / open_with_pvc           0.935 0.520 Inf    1  -0.122  1.0000
##  insitu_log / insitu_pvc       0.405 0.299 Inf    1  -1.225  0.8246
##  insitu_log / open             0.402 0.330 Inf    1  -1.110  0.8774
##  insitu_log / open_with_log    0.549 0.446 Inf    1  -0.737  0.9773
##  insitu_log / open_with_pvc    0.608 0.468 Inf    1  -0.646  0.9874
##  insitu_pvc / open             0.994 0.620 Inf    1  -0.009  1.0000
##  insitu_pvc / open_with_log    1.356 0.833 Inf    1   0.497  0.9963
##  insitu_pvc / open_with_pvc    1.503 0.829 Inf    1   0.739  0.9770
##  open / open_with_log          1.364 0.973 Inf    1   0.436  0.9980
##  open / open_with_pvc          1.512 0.999 Inf    1   0.626  0.9891
##  open_with_log / open_with_pvc 1.108 0.717 Inf    1   0.159  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## Tests are performed on the log scale
############# treatment response: do analysis for total biomass by species #############

dat3<-(dat1[c(1:4,10,14,18,22)])
totwtdat<-as.data.frame(dat3 %>% pivot_longer(c(wt_max15_goro, wt_max15_tror, wt_max15_trcy)))
totwtdat$log_wt<-log(totwtdat$value) # log transform the weight data to get it normal looking

# model
totwtmod<-lmer(log_wt~name*current_plot_type+(1|block), data=totwtdat, REML=FALSE)

# test for fit, looks pretty good
sim<-simulateResiduals(totwtmod)
plot(sim)

# model summary
summary(totwtmod)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: log_wt ~ name * current_plot_type + (1 | block)
##    Data: totwtdat
## 
##      AIC      BIC   logLik deviance df.resid 
##    586.5    649.6   -273.2    546.5      153 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3315 -0.7238  0.1635  0.6845  2.3280 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  block    (Intercept) 0.04225  0.2055  
##  Residual             1.34718  1.1607  
## Number of obs: 173, groups:  block, 7
## 
## Fixed effects:
##                                                    Estimate Std. Error
## (Intercept)                                       -2.143222   0.320185
## namewt_max15_trcy                                 -0.001833   0.439269
## namewt_max15_tror                                 -0.718937   0.456831
## current_plot_typeinsitu_log                        0.516663   0.568314
## current_plot_typeinsitu_pvc                        0.324586   0.496809
## current_plot_typeopen                              0.329858   0.425589
## current_plot_typeopen_with_log                     1.116735   0.515346
## current_plot_typeopen_with_pvc                     0.668390   0.448068
## namewt_max15_trcy:current_plot_typeinsitu_log     -0.505102   0.766320
## namewt_max15_tror:current_plot_typeinsitu_log     -0.840945   0.839782
## namewt_max15_trcy:current_plot_typeinsitu_pvc     -0.346022   0.683114
## namewt_max15_tror:current_plot_typeinsitu_pvc      0.125206   0.703008
## namewt_max15_trcy:current_plot_typeopen           -0.730280   0.644938
## namewt_max15_tror:current_plot_typeopen           -1.093074   0.751193
## namewt_max15_trcy:current_plot_typeopen_with_log  -1.679654   0.745031
## namewt_max15_tror:current_plot_typeopen_with_log  -0.693597   0.778032
## namewt_max15_trcy:current_plot_typeopen_with_pvc  -1.467203   0.657613
## namewt_max15_tror:current_plot_typeopen_with_pvc  -0.911378   0.681196
##                                                          df t value Pr(>|t|)
## (Intercept)                                      140.850563  -6.694 4.77e-10
## namewt_max15_trcy                                167.738562  -0.004   0.9967
## namewt_max15_tror                                167.112865  -1.574   0.1174
## current_plot_typeinsitu_log                      169.260569   0.909   0.3646
## current_plot_typeinsitu_pvc                      168.135800   0.653   0.5144
## current_plot_typeopen                            168.168196   0.775   0.4394
## current_plot_typeopen_with_log                   168.175199   2.167   0.0316
## current_plot_typeopen_with_pvc                   168.400310   1.492   0.1376
## namewt_max15_trcy:current_plot_typeinsitu_log    167.901796  -0.659   0.5107
## namewt_max15_tror:current_plot_typeinsitu_log    168.255431  -1.001   0.3181
## namewt_max15_trcy:current_plot_typeinsitu_pvc    168.316088  -0.507   0.6131
## namewt_max15_tror:current_plot_typeinsitu_pvc    167.790062   0.178   0.8589
## namewt_max15_trcy:current_plot_typeopen          170.379238  -1.132   0.2591
## namewt_max15_tror:current_plot_typeopen          168.159556  -1.455   0.1475
## namewt_max15_trcy:current_plot_typeopen_with_log 168.023484  -2.254   0.0255
## namewt_max15_tror:current_plot_typeopen_with_log 169.134810  -0.891   0.3739
## namewt_max15_trcy:current_plot_typeopen_with_pvc 168.137750  -2.231   0.0270
## namewt_max15_tror:current_plot_typeopen_with_pvc 168.495505  -1.338   0.1827
##                                                     
## (Intercept)                                      ***
## namewt_max15_trcy                                   
## namewt_max15_tror                                   
## current_plot_typeinsitu_log                         
## current_plot_typeinsitu_pvc                         
## current_plot_typeopen                               
## current_plot_typeopen_with_log                   *  
## current_plot_typeopen_with_pvc                      
## namewt_max15_trcy:current_plot_typeinsitu_log       
## namewt_max15_tror:current_plot_typeinsitu_log       
## namewt_max15_trcy:current_plot_typeinsitu_pvc       
## namewt_max15_tror:current_plot_typeinsitu_pvc       
## namewt_max15_trcy:current_plot_typeopen             
## namewt_max15_tror:current_plot_typeopen             
## namewt_max15_trcy:current_plot_typeopen_with_log *  
## namewt_max15_tror:current_plot_typeopen_with_log    
## namewt_max15_trcy:current_plot_typeopen_with_pvc *  
## namewt_max15_tror:current_plot_typeopen_with_pvc    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmip(totwtmod,~current_plot_type|name, CI=T)

est<-emmeans(totwtmod,~current_plot_type|name, type='response')
pairs(est)
## name = wt_max15_goro:
##  contrast                      estimate    SE  df t.ratio p.value
##  gap - insitu_log              -0.51666 0.602 188  -0.859  0.9557
##  gap - insitu_pvc              -0.32459 0.525 187  -0.618  0.9896
##  gap - open                    -0.32986 0.450 187  -0.733  0.9776
##  gap - open_with_log           -1.11673 0.545 187  -2.050  0.3183
##  gap - open_with_pvc           -0.66839 0.474 187  -1.411  0.7205
##  insitu_log - insitu_pvc        0.19208 0.650 188   0.295  0.9997
##  insitu_log - open              0.18681 0.589 187   0.317  0.9996
##  insitu_log - open_with_log    -0.60007 0.663 186  -0.905  0.9447
##  insitu_log - open_with_pvc    -0.15173 0.608 187  -0.250  0.9999
##  insitu_pvc - open             -0.00527 0.511 186  -0.010  1.0000
##  insitu_pvc - open_with_log    -0.79215 0.598 188  -1.324  0.7715
##  insitu_pvc - open_with_pvc    -0.34380 0.535 189  -0.642  0.9876
##  open - open_with_log          -0.78688 0.532 186  -1.480  0.6773
##  open - open_with_pvc          -0.33853 0.462 189  -0.733  0.9776
##  open_with_log - open_with_pvc  0.44834 0.553 187   0.811  0.9653
## 
## name = wt_max15_trcy:
##  contrast                      estimate    SE  df t.ratio p.value
##  gap - insitu_log              -0.01156 0.546 187  -0.021  1.0000
##  gap - insitu_pvc               0.02144 0.497 188   0.043  1.0000
##  gap - open                     0.40042 0.512 189   0.782  0.9703
##  gap - open_with_log            0.56292 0.568 186   0.990  0.9206
##  gap - open_with_pvc            0.79881 0.509 186   1.570  0.6193
##  insitu_log - insitu_pvc        0.03300 0.573 188   0.058  1.0000
##  insitu_log - open              0.41198 0.586 189   0.703  0.9813
##  insitu_log - open_with_log     0.57448 0.639 188   0.899  0.9463
##  insitu_log - open_with_pvc     0.81037 0.582 186   1.392  0.7315
##  insitu_pvc - open              0.37899 0.540 189   0.701  0.9816
##  insitu_pvc - open_with_log     0.54148 0.595 187   0.911  0.9434
##  insitu_pvc - open_with_pvc     0.77738 0.536 186   1.449  0.6968
##  open - open_with_log           0.16250 0.611 190   0.266  0.9998
##  open - open_with_pvc           0.39839 0.552 188   0.722  0.9790
##  open_with_log - open_with_pvc  0.23589 0.606 187   0.389  0.9988
## 
## name = wt_max15_tror:
##  contrast                      estimate    SE  df t.ratio p.value
##  gap - insitu_log               0.32428 0.656 188   0.494  0.9963
##  gap - insitu_pvc              -0.44979 0.526 187  -0.855  0.9565
##  gap - open                     0.76322 0.656 187   1.164  0.8533
##  gap - open_with_log           -0.42314 0.615 187  -0.688  0.9831
##  gap - open_with_pvc            0.24299 0.543 188   0.447  0.9977
##  insitu_log - insitu_pvc       -0.77407 0.676 189  -1.144  0.8621
##  insitu_log - open              0.43893 0.782 189   0.561  0.9934
##  insitu_log - open_with_log    -0.74742 0.748 188  -1.000  0.9175
##  insitu_log - open_with_pvc    -0.08129 0.693 191  -0.117  1.0000
##  insitu_pvc - open              1.21301 0.676 188   1.794  0.4723
##  insitu_pvc - open_with_log     0.02665 0.636 188   0.042  1.0000
##  insitu_pvc - open_with_pvc     0.69278 0.570 190   1.216  0.8286
##  open - open_with_log          -1.18635 0.749 189  -1.583  0.6107
##  open - open_with_pvc          -0.52023 0.689 189  -0.755  0.9745
##  open_with_log - open_with_pvc  0.66613 0.650 188   1.024  0.9093
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 6 estimates
############# treatment response: do analysis for per capita biomass by species #############

dat4<-(dat1[c(1:4,9,13,17,22)])
pcwtdat<-as.data.frame(dat4 %>% pivot_longer(c(wt_percapita_goro, wt_percapita_tror, wt_percapita_trcy)))
pcwtdat$log_wt<-log(pcwtdat$value) # log transform the weight data to get it normal looking

# model
pcwtmod<-lmer(log_wt~name*current_plot_type+(1|block), data=pcwtdat, REML=FALSE)

# test for fit, looks pretty good
# sim<-simulateResiduals(pcwtmod)
# plot(sim)

# model summary
summary(pcwtmod)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: log_wt ~ name * current_plot_type + (1 | block)
##    Data: pcwtdat
## 
##      AIC      BIC   logLik deviance df.resid 
##    527.8    590.8   -243.9    487.8      153 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.30924 -0.73901 -0.03115  0.63661  2.86707 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  block    (Intercept) 0.07573  0.2752  
##  Residual             0.93957  0.9693  
## Number of obs: 173, groups:  block, 7
## 
## Fixed effects:
##                                                      Estimate Std. Error
## (Intercept)                                           -2.8584     0.2797
## namewt_percapita_trcy                                 -0.2256     0.3671
## namewt_percapita_tror                                 -0.5377     0.3816
## current_plot_typeinsitu_log                            1.1314     0.4756
## current_plot_typeinsitu_pvc                            0.3094     0.4153
## current_plot_typeopen                                  0.4074     0.3558
## current_plot_typeopen_with_log                         1.2638     0.4309
## current_plot_typeopen_with_pvc                         0.6385     0.3747
## namewt_percapita_trcy:current_plot_typeinsitu_log     -1.2080     0.6405
## namewt_percapita_tror:current_plot_typeinsitu_log     -1.3322     0.7021
## namewt_percapita_trcy:current_plot_typeinsitu_pvc     -0.4013     0.5711
## namewt_percapita_tror:current_plot_typeinsitu_pvc     -0.2449     0.5876
## namewt_percapita_trcy:current_plot_typeopen           -0.6393     0.5402
## namewt_percapita_tror:current_plot_typeopen           -1.5347     0.6280
## namewt_percapita_trcy:current_plot_typeopen_with_log  -1.7482     0.6228
## namewt_percapita_tror:current_plot_typeopen_with_log  -0.9779     0.6510
## namewt_percapita_trcy:current_plot_typeopen_with_pvc  -1.1678     0.5497
## namewt_percapita_tror:current_plot_typeopen_with_pvc  -0.9233     0.5696
##                                                            df t value Pr(>|t|)
## (Intercept)                                          101.5741 -10.220  < 2e-16
## namewt_percapita_trcy                                167.1779  -0.615  0.53968
## namewt_percapita_tror                                166.7751  -1.409  0.16066
## current_plot_typeinsitu_log                          168.2573   2.379  0.01847
## current_plot_typeinsitu_pvc                          167.4481   0.745  0.45733
## current_plot_typeopen                                167.4462   1.145  0.25377
## current_plot_typeopen_with_log                       167.5329   2.933  0.00382
## current_plot_typeopen_with_pvc                       167.6177   1.704  0.09017
## namewt_percapita_trcy:current_plot_typeinsitu_log    167.2535  -1.886  0.06102
## namewt_percapita_tror:current_plot_typeinsitu_log    167.5595  -1.897  0.05949
## namewt_percapita_trcy:current_plot_typeinsitu_pvc    167.5144  -0.703  0.48322
## namewt_percapita_tror:current_plot_typeinsitu_pvc    167.2422  -0.417  0.67741
## namewt_percapita_trcy:current_plot_typeopen          168.9857  -1.183  0.23831
## namewt_percapita_tror:current_plot_typeopen          167.4504  -2.444  0.01557
## namewt_percapita_trcy:current_plot_typeopen_with_log 167.4266  -2.807  0.00559
## namewt_percapita_tror:current_plot_typeopen_with_log 168.1810  -1.502  0.13493
## namewt_percapita_trcy:current_plot_typeopen_with_pvc 167.4163  -2.124  0.03513
## namewt_percapita_tror:current_plot_typeopen_with_pvc 167.6639  -1.621  0.10694
##                                                         
## (Intercept)                                          ***
## namewt_percapita_trcy                                   
## namewt_percapita_tror                                   
## current_plot_typeinsitu_log                          *  
## current_plot_typeinsitu_pvc                             
## current_plot_typeopen                                   
## current_plot_typeopen_with_log                       ** 
## current_plot_typeopen_with_pvc                       .  
## namewt_percapita_trcy:current_plot_typeinsitu_log    .  
## namewt_percapita_tror:current_plot_typeinsitu_log    .  
## namewt_percapita_trcy:current_plot_typeinsitu_pvc       
## namewt_percapita_tror:current_plot_typeinsitu_pvc       
## namewt_percapita_trcy:current_plot_typeopen             
## namewt_percapita_tror:current_plot_typeopen          *  
## namewt_percapita_trcy:current_plot_typeopen_with_log ** 
## namewt_percapita_tror:current_plot_typeopen_with_log    
## namewt_percapita_trcy:current_plot_typeopen_with_pvc *  
## namewt_percapita_tror:current_plot_typeopen_with_pvc    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmip(pcwtmod,~current_plot_type|name,CI=T)

est<-emmeans(totwtmod,~current_plot_type|name, type='response')
pairs(est)
## name = wt_max15_goro:
##  contrast                      estimate    SE  df t.ratio p.value
##  gap - insitu_log              -0.51666 0.602 188  -0.859  0.9557
##  gap - insitu_pvc              -0.32459 0.525 187  -0.618  0.9896
##  gap - open                    -0.32986 0.450 187  -0.733  0.9776
##  gap - open_with_log           -1.11673 0.545 187  -2.050  0.3183
##  gap - open_with_pvc           -0.66839 0.474 187  -1.411  0.7205
##  insitu_log - insitu_pvc        0.19208 0.650 188   0.295  0.9997
##  insitu_log - open              0.18681 0.589 187   0.317  0.9996
##  insitu_log - open_with_log    -0.60007 0.663 186  -0.905  0.9447
##  insitu_log - open_with_pvc    -0.15173 0.608 187  -0.250  0.9999
##  insitu_pvc - open             -0.00527 0.511 186  -0.010  1.0000
##  insitu_pvc - open_with_log    -0.79215 0.598 188  -1.324  0.7715
##  insitu_pvc - open_with_pvc    -0.34380 0.535 189  -0.642  0.9876
##  open - open_with_log          -0.78688 0.532 186  -1.480  0.6773
##  open - open_with_pvc          -0.33853 0.462 189  -0.733  0.9776
##  open_with_log - open_with_pvc  0.44834 0.553 187   0.811  0.9653
## 
## name = wt_max15_trcy:
##  contrast                      estimate    SE  df t.ratio p.value
##  gap - insitu_log              -0.01156 0.546 187  -0.021  1.0000
##  gap - insitu_pvc               0.02144 0.497 188   0.043  1.0000
##  gap - open                     0.40042 0.512 189   0.782  0.9703
##  gap - open_with_log            0.56292 0.568 186   0.990  0.9206
##  gap - open_with_pvc            0.79881 0.509 186   1.570  0.6193
##  insitu_log - insitu_pvc        0.03300 0.573 188   0.058  1.0000
##  insitu_log - open              0.41198 0.586 189   0.703  0.9813
##  insitu_log - open_with_log     0.57448 0.639 188   0.899  0.9463
##  insitu_log - open_with_pvc     0.81037 0.582 186   1.392  0.7315
##  insitu_pvc - open              0.37899 0.540 189   0.701  0.9816
##  insitu_pvc - open_with_log     0.54148 0.595 187   0.911  0.9434
##  insitu_pvc - open_with_pvc     0.77738 0.536 186   1.449  0.6968
##  open - open_with_log           0.16250 0.611 190   0.266  0.9998
##  open - open_with_pvc           0.39839 0.552 188   0.722  0.9790
##  open_with_log - open_with_pvc  0.23589 0.606 187   0.389  0.9988
## 
## name = wt_max15_tror:
##  contrast                      estimate    SE  df t.ratio p.value
##  gap - insitu_log               0.32428 0.656 188   0.494  0.9963
##  gap - insitu_pvc              -0.44979 0.526 187  -0.855  0.9565
##  gap - open                     0.76322 0.656 187   1.164  0.8533
##  gap - open_with_log           -0.42314 0.615 187  -0.688  0.9831
##  gap - open_with_pvc            0.24299 0.543 188   0.447  0.9977
##  insitu_log - insitu_pvc       -0.77407 0.676 189  -1.144  0.8621
##  insitu_log - open              0.43893 0.782 189   0.561  0.9934
##  insitu_log - open_with_log    -0.74742 0.748 188  -1.000  0.9175
##  insitu_log - open_with_pvc    -0.08129 0.693 191  -0.117  1.0000
##  insitu_pvc - open              1.21301 0.676 188   1.794  0.4723
##  insitu_pvc - open_with_log     0.02665 0.636 188   0.042  1.0000
##  insitu_pvc - open_with_pvc     0.69278 0.570 190   1.216  0.8286
##  open - open_with_log          -1.18635 0.749 189  -1.583  0.6107
##  open - open_with_pvc          -0.52023 0.689 189  -0.755  0.9745
##  open_with_log - open_with_pvc  0.66613 0.650 188   1.024  0.9093
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 6 estimates
######### What about the log legacy - initial treatment for 2021 #######

############# log legacy response: do analysis for count by species ############# 
fit3_leg<-glmmTMB(value~name*initial+(1 | block), ziformula=~., family=nbinom2(), data=countdat, REML=FALSE)

## This stuff will just give you the end result counts with the zeros factored in...
summary(fit3_leg)
##  Family: nbinom2  ( log )
## Formula:          value ~ name * initial + (1 | block)
## Zero inflation:         ~.
## Data: countdat
## 
##      AIC      BIC   logLik deviance df.resid 
##   1085.9   1143.2   -528.0   1055.9      321 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.008194 0.09052 
## Number of obs: 336, groups:  block, 7
## 
## Zero-inflation model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.3856   0.621   
## Number of obs: 336, groups:  block, 7
## 
## Dispersion parameter for nbinom2 family (): 1.34 
## 
## Conditional model:
##                       Estimate Std. Error z value Pr(>|z|)   
## (Intercept)             0.2274     0.2425   0.938  0.34834   
## namentrcy               0.9312     0.2900   3.211  0.00132 **
## namentror               0.2516     0.3176   0.792  0.42839   
## initialopen             0.1751     0.2955   0.592  0.55357   
## namentrcy:initialopen  -0.6549     0.4113  -1.592  0.11131   
## namentror:initialopen  -0.1735     0.4673  -0.371  0.71045   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##                       Estimate Std. Error z value Pr(>|z|)
## (Intercept)             -2.125      1.490  -1.427    0.154
## namentrcy                1.030      1.413   0.729    0.466
## namentror                1.078      1.456   0.741    0.459
## initialopen             -2.851     14.373  -0.198    0.843
## namentrcy:initialopen    3.113     14.414   0.216    0.829
## namentror:initialopen    3.645     14.425   0.253    0.801
emmip(fit3_leg,~initial|name, type='response',CI=T)

est<-emmeans(fit3_leg,~initial|name, type='response')
pairs(est)
## name = ngoro:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 0.839 0.248 Inf    1  -0.592  0.5536
## 
## name = ntrcy:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 1.616 0.459 Inf    1   1.689  0.0912
## 
## name = ntror:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 0.998 0.356 Inf    1  -0.004  0.9964
## 
## Tests are performed on the log scale
#### split up occurrence and abundance
### zeros and ones 
zerofit_leg<-glmmTMB(presence~name*initial+(1 | block), family=binomial, data=countdat, REML=FALSE)
emmip(zerofit_leg,~initial|name, type='response',CI=T)

est<-emmeans(zerofit_leg, ~initial|name, type='response')
pairs(est)
## name = ngoro:
##  contrast   odds.ratio    SE  df null z.ratio p.value
##  log / open       0.54 0.214 Inf    1  -1.557  0.1195
## 
## name = ntrcy:
##  contrast   odds.ratio    SE  df null z.ratio p.value
##  log / open       1.57 0.608 Inf    1   1.155  0.2482
## 
## name = ntror:
##  contrast   odds.ratio    SE  df null z.ratio p.value
##  log / open       1.71 0.671 Inf    1   1.359  0.1740
## 
## Tests are performed on the log odds ratio scale
### abundance with a truncated negbinom 
countfit_leg<-glmmTMB(posicounts~name*initial+(1 | block), family=truncated_nbinom2(), data=countdat, REML=FALSE)
emmip(countfit_leg,~initial|name, type='response',CI=T)

est<-emmeans(countfit_leg, ~initial|name, type='response')
pairs(est)
## name = ngoro:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 0.912 0.304 Inf    1  -0.276  0.7826
## 
## name = ntrcy:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 1.646 0.508 Inf    1   1.615  0.1062
## 
## name = ntror:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 0.931 0.350 Inf    1  -0.190  0.8490
## 
## Tests are performed on the log scale
############# log legacy response: do analysis for total weight by species ############# 

# weights
totwtmod_leg<-lmer(log_wt~name*initial+(1|block), data=totwtdat, REML=FALSE)

# model summary
summary(totwtmod_leg)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: log_wt ~ name * initial + (1 | block)
##    Data: totwtdat
## 
##      AIC      BIC   logLik deviance df.resid 
##    570.9    596.1   -277.4    554.9      165 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4315 -0.7041  0.1434  0.6437  2.4237 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  block    (Intercept) 0.05212  0.2283  
##  Residual             1.40964  1.1873  
## Number of obs: 173, groups:  block, 7
## 
## Fixed effects:
##                               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)                    -1.9303     0.2375  73.5394  -8.129  7.7e-12 ***
## namewt_max15_trcy              -0.2174     0.3028 168.0694  -0.718   0.4737    
## namewt_max15_tror              -0.8242     0.3184 168.6561  -2.588   0.0105 *  
## initialopen                     0.4106     0.2948 167.5987   1.393   0.1655    
## namewt_max15_trcy:initialopen  -0.9973     0.4269 167.9272  -2.336   0.0207 *  
## namewt_max15_tror:initialopen  -0.6913     0.4589 168.6129  -1.506   0.1338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                (Intr) nmwt_mx15_trc nmwt_mx15_trr intlpn nmwt_mx15_trc:
## nmwt_mx15_trc  -0.679                                                  
## nmwt_mx15_trr  -0.645  0.508                                           
## initialopen    -0.696  0.546         0.518                             
## nmwt_mx15_trc:  0.479 -0.707        -0.357        -0.691               
## nmwt_mx15_trr:  0.446 -0.352        -0.693        -0.641  0.443
emmip(totwtmod_leg,~initial|name,CI=T)

est<-emmeans(totwtmod_leg,~initial|name, type='response')
pairs(est)
## name = wt_max15_goro:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open   -0.411 0.300 172  -1.370  0.1723
## 
## name = wt_max15_trcy:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open    0.587 0.314 172   1.870  0.0632
## 
## name = wt_max15_tror:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open    0.281 0.359 175   0.782  0.4355
## 
## Degrees-of-freedom method: kenward-roger
############# log legacy response: do analysis for per capita weight by species ############# 

# model
pcwtmod_leg<-lmer(log_wt~name*initial+(1|block), data=pcwtdat, REML=FALSE)

# test for fit, looks pretty good
# sim<-simulateResiduals(pcwtmod_leg)
# plot(sim)

# model summary
summary(pcwtmod_leg)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: log_wt ~ name * initial + (1 | block)
##    Data: pcwtdat
## 
##      AIC      BIC   logLik deviance df.resid 
##    519.5    544.7   -251.7    503.5      165 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.03880 -0.72942 -0.05825  0.70998  2.68818 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  block    (Intercept) 0.07959  0.2821  
##  Residual             1.03008  1.0149  
## Number of obs: 173, groups:  block, 7
## 
## Fixed effects:
##                                   Estimate Std. Error       df t value Pr(>|t|)
## (Intercept)                        -2.5260     0.2173  48.0659 -11.624 1.44e-15
## namewt_percapita_trcy              -0.6070     0.2590 167.4836  -2.344  0.02026
## namewt_percapita_tror              -0.8828     0.2725 167.9442  -3.240  0.00144
## initialopen                         0.3420     0.2521 167.1927   1.357  0.17664
## namewt_percapita_trcy:initialopen  -0.6973     0.3651 167.4033  -1.910  0.05787
## namewt_percapita_tror:initialopen  -0.6557     0.3927 167.8602  -1.670  0.09683
##                                      
## (Intercept)                       ***
## namewt_percapita_trcy             *  
## namewt_percapita_tror             ** 
## initialopen                          
## namewt_percapita_trcy:initialopen .  
## namewt_percapita_tror:initialopen .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                 (Intr) nmwt_prcpt_trc nmwt_prcpt_trr intlpn nmwt_prcpt_trc:
## nmwt_prcpt_trc  -0.635                                                     
## nmwt_prcpt_trr  -0.602  0.509                                              
## initialopen     -0.650  0.546          0.517                               
## nmwt_prcpt_trc:  0.447 -0.706         -0.356         -0.691                
## nmwt_prcpt_trr:  0.416 -0.352         -0.693         -0.640  0.443
emmip(pcwtmod_leg,~initial|name,CI=T)

est<-emmeans(pcwtmod_leg,~initial|name, type='response')
pairs(est)
## name = wt_percapita_goro:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open   -0.342 0.256 172  -1.335  0.1835
## 
## name = wt_percapita_trcy:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open    0.355 0.268 172   1.324  0.1871
## 
## name = wt_percapita_tror:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open    0.314 0.307 174   1.021  0.3086
## 
## Degrees-of-freedom method: kenward-roger
######### What about the physical barrier treatment for 2021 #######

############# physical barrier response: do analysis for count by species ############# 
fit3_phys<-glmmTMB(value~name*physical_barrier+(1 | block), ziformula=~., family=nbinom2(), data=countdat, REML=FALSE)

## This stuff will just give you the end result counts with the zeros factored in...
summary(fit3_phys)
##  Family: nbinom2  ( log )
## Formula:          value ~ name * physical_barrier + (1 | block)
## Zero inflation:         ~.
## Data: countdat
## 
##      AIC      BIC   logLik deviance df.resid 
##   1081.4   1138.6   -525.7   1051.4      321 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.01393  0.118   
## Number of obs: 336, groups:  block, 7
## 
## Zero-inflation model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.6455   0.8034  
## Number of obs: 336, groups:  block, 7
## 
## Dispersion parameter for nbinom2 family (): 1.25 
## 
## Conditional model:
##                             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                  0.35230    0.22855   1.542   0.1232  
## namentrcy                    0.63881    0.31276   2.042   0.0411 *
## namentror                    0.05297    0.36846   0.144   0.8857  
## physical_barrier1           -0.06418    0.27850  -0.230   0.8177  
## namentrcy:physical_barrier1 -0.01190    0.40264  -0.030   0.9764  
## namentror:physical_barrier1  0.11757    0.45074   0.261   0.7942  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##                             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                   -2.222      1.377  -1.613   0.1067  
## namentrcy                      1.911      1.330   1.436   0.1509  
## namentror                      2.226      1.331   1.673   0.0944 .
## physical_barrier1            -15.234   4791.566  -0.003   0.9975  
## namentrcy:physical_barrier1   13.581   4791.566   0.003   0.9977  
## namentror:physical_barrier1   13.477   4791.566   0.003   0.9978  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmip(fit3_phys,~physical_barrier|name, type='response',CI=T)

est<-emmeans(fit3_phys,~physical_barrier|name, type='response')
pairs(est)
## name = ngoro:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.066 0.297 Inf    1   0.230  0.8177
## 
## name = ntrcy:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.079 0.311 Inf    1   0.264  0.7916
## 
## name = ntror:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.948 0.346 Inf    1  -0.146  0.8837
## 
## Tests are performed on the log scale
#### split up occurrence and abundance
### zeros and ones 
zerofit_phys<-glmmTMB(presence~name*physical_barrier+(1 | block), family=binomial, data=countdat, REML=FALSE)
emmip(zerofit_phys,~physical_barrier|name, type='response',CI=T)

est<-emmeans(zerofit_phys, ~physical_barrier|name, type='response')
pairs(est)
## name = ngoro:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.692 0.272 Inf    1  -0.936
##  p.value
##   0.3491
## 
## name = ntrcy:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.439 0.173 Inf    1  -2.089
##  p.value
##   0.0367
## 
## name = ntror:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.390 0.157 Inf    1  -2.342
##  p.value
##   0.0192
## 
## Tests are performed on the log odds ratio scale
### abundance with a truncated negbinom 
countfit_phys<-glmmTMB(posicounts~name*physical_barrier+(1 | block), family=truncated_nbinom2(), data=countdat, REML=FALSE)
emmip(countfit_phys,~physical_barrier|name, type='response',CI=T)

est<-emmeans(countfit_phys, ~physical_barrier|name, type='response')
pairs(est)
## name = ngoro:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1  1.17 0.395 Inf    1   0.475  0.6349
## 
## name = ntrcy:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1  1.06 0.333 Inf    1   0.174  0.8618
## 
## name = ntror:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1  0.98 0.388 Inf    1  -0.050  0.9597
## 
## Tests are performed on the log scale
############# physical barrier response: do analysis for total weight by species ############# 

# weights
totwtmod_phys<-lmer(log_wt~name*physical_barrier+(1|block), data=totwtdat, REML=FALSE)

# model summary
summary(totwtmod_phys)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: log_wt ~ name * physical_barrier + (1 | block)
##    Data: totwtdat
## 
##      AIC      BIC   logLik deviance df.resid 
##    573.2    598.5   -278.6    557.2      165 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5416 -0.7613  0.1349  0.6948  2.0654 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  block    (Intercept) 0.04565  0.2137  
##  Residual             1.43320  1.1972  
## Number of obs: 173, groups:  block, 7
## 
## Fixed effects:
##                                     Estimate Std. Error       df t value
## (Intercept)                          -1.9660     0.2339  74.7835  -8.405
## namewt_max15_trcy                    -0.3461     0.3295 170.1738  -1.050
## namewt_max15_tror                    -1.1195     0.3640 168.1650  -3.076
## physical_barrier1                     0.4796     0.2966 168.3691   1.617
## namewt_max15_trcy:physical_barrier1  -0.6470     0.4334 168.3829  -1.493
## namewt_max15_tror:physical_barrier1  -0.1491     0.4693 167.9327  -0.318
##                                     Pr(>|t|)    
## (Intercept)                         2.06e-12 ***
## namewt_max15_trcy                    0.29502    
## namewt_max15_tror                    0.00245 ** 
## physical_barrier1                    0.10769    
## namewt_max15_trcy:physical_barrier1  0.13741    
## namewt_max15_tror:physical_barrier1  0.75110    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                  (Intr) nmwt_mx15_trc nmwt_mx15_trr phys_1 nmwt_mx15_trc:_1
## nmwt_mx15_trc    -0.624                                                    
## nmwt_mx15_trr    -0.562  0.403                                             
## physcl_brr1      -0.692  0.489         0.443                               
## nmwt_mx15_trc:_1  0.473 -0.758        -0.305        -0.683                 
## nmwt_mx15_trr:_1  0.434 -0.310        -0.774        -0.631  0.432
emmip(totwtmod_phys,~physical_barrier|name,CI=T)

est<-emmeans(totwtmod_phys,~physical_barrier|name, type='response')
pairs(est)
## name = wt_max15_goro:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.480 0.302 173  -1.589  0.1138
## 
## name = wt_max15_trcy:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1    0.167 0.323 174   0.519  0.6048
## 
## name = wt_max15_tror:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.331 0.371 173  -0.892  0.3736
## 
## Degrees-of-freedom method: kenward-roger
############# physical barrier response: do analysis for per capita weight by species ############# 

# model
pcwtmod_phys<-lmer(log_wt~name*physical_barrier+(1|block), data=pcwtdat, REML=FALSE) 

# test for fit, looks pretty good
# sim<-simulateResiduals(pcwtmod_leg)
# plot(sim)

# model summary
summary(pcwtmod_phys)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
##   method [lmerModLmerTest]
## Formula: log_wt ~ name * physical_barrier + (1 | block)
##    Data: pcwtdat
## 
##      AIC      BIC   logLik deviance df.resid 
##    517.9    543.2   -251.0    501.9      165 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4634 -0.6793 -0.0181  0.6950  2.6000 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  block    (Intercept) 0.07667  0.2769  
##  Residual             1.02169  1.0108  
## Number of obs: 173, groups:  block, 7
## 
## Fixed effects:
##                                         Estimate Std. Error       df t value
## (Intercept)                              -2.6399     0.2132  46.7683 -12.384
## namewt_percapita_trcy                    -0.5377     0.2789 168.9358  -1.928
## namewt_percapita_tror                    -1.0875     0.3076 167.5235  -3.535
## physical_barrier1                         0.5559     0.2506 167.6282   2.218
## namewt_percapita_trcy:physical_barrier1  -0.7494     0.3663 167.6441  -2.046
## namewt_percapita_tror:physical_barrier1  -0.2664     0.3965 167.3819  -0.672
##                                         Pr(>|t|)    
## (Intercept)                             2.27e-16 ***
## namewt_percapita_trcy                   0.055547 .  
## namewt_percapita_tror                   0.000527 ***
## physical_barrier1                       0.027889 *  
## namewt_percapita_trcy:physical_barrier1 0.042344 *  
## namewt_percapita_tror:physical_barrier1 0.502654    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                   (Intr) nmwt_prcpt_trc nmwt_prcpt_trr phys_1 nmwt_prcpt_trc:_1
## nmwt_prcpt_trc    -0.578                                                       
## nmwt_prcpt_trr    -0.520  0.404                                                
## physcl_brr1       -0.642  0.488          0.443                                 
## nmwt_prcpt_trc:_1  0.439 -0.758         -0.306         -0.682                  
## nmwt_prcpt_trr:_1  0.402 -0.309         -0.774         -0.631  0.432
emmip(pcwtmod_phys,~physical_barrier|name,CI=T)

est<-emmeans(pcwtmod_phys,~physical_barrier|name, type='response')
pairs(est)
## name = wt_percapita_goro:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.556 0.255 172  -2.182  0.0305
## 
## name = wt_percapita_trcy:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1    0.193 0.273 173   0.710  0.4789
## 
## name = wt_percapita_tror:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.290 0.313 172  -0.926  0.3560
## 
## Degrees-of-freedom method: kenward-roger
#### What about both physical and legacy? #### 
### zeros and ones 
zerofit_intxn<-glmmTMB(presence~name*physical_barrier*initial+(1 | block), family=binomial, data=countdat, REML=FALSE)

sim<-simulateResiduals(zerofit_intxn)
plot(sim)

testDispersion(sim)

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0104, p-value = 0.76
## alternative hypothesis: two.sided
testZeroInflation(sim)

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.0049, p-value = 0.928
## alternative hypothesis: two.sided
emmip(zerofit_intxn,initial~physical_barrier|name, type='response',CI=T)

est<-emmeans(zerofit_intxn, ~physical_barrier|name|initial, type='response')
pairs(est)
## name = ngoro, initial = log:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.862 0.470 Inf    1  -0.273
##  p.value
##   0.7851
## 
## name = ntrcy, initial = log:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.460 0.260 Inf    1  -1.375
##  p.value
##   0.1690
## 
## name = ntror, initial = log:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.639 0.351 Inf    1  -0.817
##  p.value
##   0.4142
## 
## name = ngoro, initial = open:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.544 0.317 Inf    1  -1.045
##  p.value
##   0.2958
## 
## name = ntrcy, initial = open:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.402 0.225 Inf    1  -1.628
##  p.value
##   0.1036
## 
## name = ntror, initial = open:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.201 0.127 Inf    1  -2.544
##  p.value
##   0.0110
## 
## Tests are performed on the log odds ratio scale
### abundance with a truncated negbinom 
countfit_intxn<-glmmTMB(posicounts~name*physical_barrier*initial+(1 | block), family=truncated_nbinom2(), data=countdat, REML=FALSE)

sim<-simulateResiduals(countfit_intxn)
plot(sim)

testDispersion(sim)

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0637, p-value = 0.648
## alternative hypothesis: two.sided
testZeroInflation(sim)

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sided
emmip(countfit_intxn,~physical_barrier|name|initial, type='response',CI=T)

est<-emmeans(countfit_intxn, ~physical_barrier|name|initial, type='response')
pairs(est)
## name = ngoro, initial = log:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.448 0.727 Inf    1   0.738  0.4603
## 
## name = ntrcy, initial = log:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.080 0.432 Inf    1   0.192  0.8479
## 
## name = ntror, initial = log:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.778 0.387 Inf    1  -0.506  0.6132
## 
## name = ngoro, initial = open:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.015 0.444 Inf    1   0.033  0.9735
## 
## name = ntrcy, initial = open:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.944 0.467 Inf    1  -0.116  0.9080
## 
## name = ntror, initial = open:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.447 0.912 Inf    1   0.586  0.5580
## 
## Tests are performed on the log scale
### additive examp,es
zerofit_add<-glmmTMB(presence~name*physical_barrier+name*initial+(1 | block), family=binomial, data=countdat, REML=FALSE)

countfit_add<-glmmTMB(posicounts~name*physical_barrier+name*initial+(1 | block), family=truncated_nbinom2(), data=countdat, REML=FALSE)

### per capita biomass 

# model - intxn
pcwtmod_intxn<-lmer(log_wt~name*physical_barrier*initial+(1|block), data=pcwtdat, REML=FALSE)

# model - no intxn
pcwtmod_add<-lmer(log_wt~name*physical_barrier+name*initial+(1|block), data=pcwtdat, REML=FALSE)


#### 2021 model comparison for counts #### 
## zeros 
zero_candmods<-list("Plot type"=zerofit, 
                    "Physical barrier"=zerofit_phys,
                    "Nutrient island"=zerofit_leg,
                    "Physical Barrier + Nutrient Island"=zerofit_add,
                    "Physical Barrier x Nutrient Island"=zerofit_intxn)
aictab(zero_candmods) 
## 
## Model selection based on AICc:
## 
##                                     K   AICc Delta_AICc AICcWt Cum.Wt      LL
## Physical barrier                    7 457.58       0.00   0.49   0.49 -221.62
## Physical Barrier + Nutrient Island 10 457.90       0.32   0.42   0.91 -218.61
## Physical Barrier x Nutrient Island 13 462.04       4.46   0.05   0.96 -217.45
## Nutrient island                     7 462.89       5.31   0.03   1.00 -224.27
## Plot type                          19 466.87       9.30   0.00   1.00 -213.23
pairs(emmeans(zerofit_add, ~physical_barrier|name|initial))
## name = ngoro, initial = log:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.365 0.397 Inf  -0.918  0.3584
## 
## name = ntrcy, initial = log:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.844 0.398 Inf  -2.122  0.0338
## 
## name = ntror, initial = log:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.971 0.407 Inf  -2.385  0.0171
## 
## name = ngoro, initial = open:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.365 0.397 Inf  -0.918  0.3584
## 
## name = ntrcy, initial = open:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.844 0.398 Inf  -2.122  0.0338
## 
## name = ntror, initial = open:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.971 0.407 Inf  -2.385  0.0171
## 
## Results are given on the log odds ratio (not the response) scale.
# best fit model is a tie between physical barrier and additive model (deltaAIC=0.32)
# i will show results from additive model. 

# counts
count_candmods<-list("Plot type"=countfit, 
                     "Physical barrier"=countfit_phys,
                     "Nutrient island"=countfit_leg,
                     "Physical Barrier + Nutrient Island"=countfit_add,
                     "Physical Barrier x Nutrient Island"=countfit_intxn)
aictab(count_candmods) 
## 
## Model selection based on AICc:
## 
##                                     K   AICc Delta_AICc AICcWt Cum.Wt      LL
## Nutrient island                     8 621.48       0.00   0.75   0.75 -302.30
## Physical barrier                    8 623.89       2.41   0.22   0.97 -303.51
## Physical Barrier + Nutrient Island 11 627.98       6.50   0.03   1.00 -302.17
## Physical Barrier x Nutrient Island 14 634.07      12.59   0.00   1.00 -301.71
## Plot type                          20 640.85      19.37   0.00   1.00 -297.66
pairs(emmeans(countfit_leg, ~initial|name))
## name = ngoro:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open  -0.0920 0.333 Inf  -0.276  0.7826
## 
## name = ntrcy:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open   0.4984 0.309 Inf   1.615  0.1062
## 
## name = ntror:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open  -0.0716 0.376 Inf  -0.190  0.8490
## 
## Results are given on the log (not the response) scale.
# best fit model is nutrient island (next best deltaAIC=2.41 and it's physical barrier.)
# I will show the results for nutrient island. 


#### 2021 model comparison for per capita biomass #### 
pcwt_candmods<-list("Plot type"=pcwtmod, 
                    "Physical barrier"=pcwtmod_phys,
                    "Nutrient island"=pcwtmod_leg,
                    "Physical Barrier + Nutrient Island" = pcwtmod_add,
                    "Physical Barrier x Nutrient Island"=pcwtmod_intxn)
aictab(pcwt_candmods) 
## 
## Model selection based on AICc:
## 
##                                     K   AICc Delta_AICc AICcWt Cum.Wt      LL
## Physical barrier                    8 518.81       0.00   0.52   0.52 -250.97
## Nutrient island                     8 520.36       1.55   0.24   0.75 -251.74
## Physical Barrier + Nutrient Island 11 520.57       1.76   0.21   0.97 -248.47
## Physical Barrier x Nutrient Island 14 524.43       5.62   0.03   1.00 -246.88
## Plot type                          20 533.29      14.48   0.00   1.00 -243.88
pairs(emmeans(pcwtmod_phys, ~physical_barrier|name))
## name = wt_percapita_goro:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.556 0.255 172  -2.182  0.0305
## 
## name = wt_percapita_trcy:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1    0.193 0.273 173   0.710  0.4789
## 
## name = wt_percapita_tror:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.290 0.313 172  -0.926  0.3560
## 
## Degrees-of-freedom method: kenward-roger
# best fit model is physical barrier by a hair, then nutrient island, then additive model. They are all within striking distance. 
# deltaAIC physical barrier - nutrient island = 1.55
# deltaAIC physical - nutrient island = 1.76
# I will show the results for addivive model. 


### figures ###
# best fit models - zeros 
# colors
mimiscols<-c("#D66972","#108780")
# zerofit_add<-glmmTMB(presence~name*physical_barrier+name*initial+(1 | block), family=binomial, data=countdat, REML=FALSE)

zfit_est<-as.data.frame(emmeans(zerofit_add,~initial|physical_barrier|name, type='response'))
zfit_est$name<-c(rep("ngoro", 4), rep("ntrcy",4), rep("ntror",4))

pl1<-ggplot(zfit_est,aes(physical_barrier, prob,group=initial),)+
  scale_color_manual(values=mimiscols)+
  geom_point(aes(col=initial), size=2, position=position_dodge(width=0.5))+
  geom_linerange(aes(ymin=asymp.LCL, ymax=asymp.UCL, col=initial), position=position_dodge(width=0.5))+
  geom_line(aes(col=initial), position=position_dodge(width=0.5))+
  facet_wrap(vars(name))+
  theme_bw()+
  theme(strip.text.x = element_text(size=0),
        strip.background = element_blank(),
        axis.text=element_text(size=15),
        axis.title=element_text(size=20),
        legend.text=element_text(size=15),
        legend.title=element_text(size=15),
        legend.position="top")+
  xlab("Physical Barrier")+
  ylab("Occurrence")+
  geom_jitter(data=countdat,
             aes(x=physical_barrier, y=presence, color=initial), 
             height=0.1,
             alpha=0.5)+
  labs(color = "Initial Plot Type")
  
# best fit model - counts 
# countfit_leg<-glmmTMB(posicounts~name*initial+(1 | block), family=truncated_nbinom2(), data=countdat, REML=FALSE)

cfit_est<-as.data.frame(emmeans(countfit_leg,~initial|name, type='response'))
cfit_est$name<-c(rep("ngoro", 2), rep("ntrcy", 2), rep("ntror",2))

pl2<-ggplot(cfit_est,aes(initial, response, group=1))+
  geom_jitter(data=countdat,
              aes(x=initial, y=posicounts),
              width=0.1,
              alpha=0.4,
              color="gray")+
  geom_point(size=2, color="black")+
  geom_linerange(aes(ymin=asymp.LCL, ymax=asymp.UCL),color="black")+
  geom_line(color="black")+
  facet_wrap(vars(name))+
  theme_bw()+
  theme(strip.text.x = element_text(size=0),
        strip.background = element_blank(),
        axis.text=element_text(size=15),
        axis.title=element_text(size=20),
        legend.text=element_text(size=15),
        legend.title=element_text(size=15))+
  xlab("Initial Plot Type")+
  ylab("Abundance")+
  labs(color = "Initial Plot \n Type")

# best fit model - pcbiomass
# pcwtmod_phys<-lmer(log_wt~name*physical_barrier+(1|block), data=pcwtdat, REML=FALSE)

pcbfit_est<-as.data.frame(emmeans(pcwtmod_add,~physical_barrier|name|initial, type='response'))


pl3<-ggplot(pcbfit_est,aes(physical_barrier, emmean, group=initial))+
  scale_color_manual(values=mimiscols)+
  geom_point(aes(col=initial), size=2, position=position_dodge(width=0.5))+
  geom_linerange(aes(ymin=lower.CL, ymax=upper.CL, col=initial), position=position_dodge(width=0.5))+
  geom_line(aes(col=initial), position=position_dodge(width=0.5))+
  facet_wrap(vars(name))+
  theme_bw()+
  theme(strip.text.x = element_text(size=0),
        strip.background = element_blank(),
        axis.text=element_text(size=15),
        axis.title=element_text(size=20),
        legend.text=element_text(size=15),
        legend.title=element_text(size=15),
        legend.position="top")+
  xlab("Physical Barrier")+
  ylab("log(Biomass)")+
  geom_jitter(data=pcwtdat,
              aes(x=physical_barrier, y=log_wt, color=initial), 
              height=0.1,
              alpha=0.5)+
  labs(color = "Initial Plot Type")
# pl3
### altogether now
ggarrange(pl1, pl2, pl3, ncol=1, common.legend = T)

2022
# packages 
require(lme4)
require(emmeans)
require(pscl)
require(glmmTMB)
require(tidyr)
require(DHARMa)
require(ggplot2)
require(AICcmodavg)
require(ggpubr)

############## 2022 data ##############

# read csv
dat<-read.csv('nplants_data_2022.csv', header=T)
dat1<-dat[which(dat$seeding_trt==1),]
dat1$physical_barrier<-as.factor(dat1$physical_barrier)
dat1$block<-as.factor(dat1$block)
names(dat1)
##  [1] "block"               "transect"            "initial"            
##  [4] "current_plot_type"   "dispersal_treatment" "seeding_trt"        
##  [7] "ntrcy_germ"          "ngoro_germ"          "ntror_germ"         
## [10] "ntrcy_plants"        "ngoro_plants"        "ntror_plants"       
## [13] "ntrcy_biomass"       "ngoro_biomass"       "ntror_biomass"      
## [16] "ntrcy_seeds"         "ngoro_seeds"         "ntror_seeds"        
## [19] "physical_barrier"    "wt_percapita_trcy"   "wt_max15_trcy"      
## [22] "wt_percapita_goro"   "wt_max15_goro"       "wt_percapita_tror"  
## [25] "wt_max15_tror"       "ntrcy_tot"           "ngoro_tot"          
## [28] "ntror_tot"           "notes"               "to.do"              
## [31] "tree.fallen"         "tree.cleared"
# subset
dat2<-dat1[,c(1,2:4,26:28,19)] # these are block, transect, initial, current_plot_type, ngoro_plants, ntrcy_plants, ntror_plants, physical_barrier
head(dat2)
##   block transect initial current_plot_type ntrcy_tot ngoro_tot ntror_tot
## 1     1     1.01     log               gap         1         1         4
## 2     1      1.4     log               gap         2         2         1
## 3     1      1.6     log               gap         2         2         1
## 4     1      1.8     log               gap         1         0         3
## 5     1     1.02     log        insitu_log         8         0         0
## 6     1     1.14     log        insitu_log         2         1         0
##   physical_barrier
## 1                0
## 2                0
## 3                0
## 4                0
## 5                1
## 6                1
## because of the way weeding worked, where we thinned only once and probably too early in the season, there were cases where plants came up, we thinned them, and then when we collected plants at the end of the growing season there were more that popped up. 
# I am choosing to use the total number of a species that popped up in the zone of planting. this required a somewhat complex excel formula that did not double-count the one individual when summing the total, and also accounts for the plants that came up but
# did not survive. This value is called 'tot' (e.g. ngoro_tot). 
# It's impossible to get the same dataset as the one from 2021 because we planted the plants and only came back when it was time to collect at the end of the season, thus capturing both survival through the season and germination. 
# to get the total in the "tot" columns, I wrote a formula in excel, written below
# total = if((t0+t2=0),0,if(t0=0,t2,if(t2=0,t0, if(t0+t2=1,1,(t0-1+t2)))))
# in this expression t0 is the germ value (e.g. ngoro_germ) and t2 is the plants value (e.g. ngoro_plants). 
# I can run the analysis with germ, plants, or tot. I'm choosing to do the analysis on tot, but we can revisit this choice if we feel there's a better way to comparably (or not) analyze the data between 2021 and 2023. 
 
# pivot
dat22<-as.data.frame(dat2 %>% pivot_longer(c(ntrcy_tot, ngoro_tot, ntror_tot)))
range(dat22$value)
## [1]  0 25
dat22$value>15 # one sample is larger than 15, it is a tror.
##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# max out at 15 
dat22$value<-as.numeric(ifelse(dat22$value>15, 15, dat22$value))



#################################### TREATMENT RESPONSE PRELIM ANALYSIS ####################################
############# treatment response: do analysis for count by species ############# 

# I am going to do the analysis as in 2021 now, with final counts and biomass. 
countmod<-glmmTMB(value~name*current_plot_type+(1|block), family="poisson", data=dat22)

# test for fit and zero inflation
sim<-simulateResiduals(countmod)
testZeroInflation(sim) # zero-inflated so need something else! 

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.6664, p-value < 2.2e-16
## alternative hypothesis: two.sided
plot(sim) 

## going to do a hurdle model, which assumes a zero is only generated in one way 
# https://jsdajournal.springeropen.com/articles/10.1186/s40488-021-00121-4
# #https://stats.stackexchange.com/questions/81457/what-is-the-difference-between-zero-inflated-and-hurdle-models

# hurdle model 
# using examples as presented here: https://www.biorxiv.org/content/biorxiv/suppl/2017/05/01/132753.DC1/132753-2.pdf 
fit3<-glmmTMB(value~name+current_plot_type+(1|block), ziformula=~., family=nbinom2(), data=dat22) # model has non-positive definite hessian matrix when the model expression contains an interaction, so made it additive.
 sim3<-simulateResiduals(fit3)
 plot(sim3)

 testDispersion(sim3) # looks ok ! 

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.98275, p-value = 0.968
## alternative hypothesis: two.sided
 testZeroInflation(sim3) # looks ok ! 

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 0.96353, p-value = 0.792
## alternative hypothesis: two.sided
summary(fit3)
##  Family: nbinom2  ( log )
## Formula:          value ~ name + current_plot_type + (1 | block)
## Zero inflation:         ~.
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##   1229.7   1302.3   -595.9   1191.7      317 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.007064 0.08405 
## Number of obs: 336, groups:  block, 7
## 
## Zero-inflation model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 2.217    1.489   
## Number of obs: 336, groups:  block, 7
## 
## Dispersion parameter for nbinom2 family (): 1.53 
## 
## Conditional model:
##                                Estimate Std. Error z value Pr(>|z|)   
## (Intercept)                     0.46596    0.16852   2.765  0.00569 **
## namentrcy_tot                   0.07124    0.16537   0.431  0.66664   
## namentror_tot                   0.54253    0.16727   3.243  0.00118 **
## current_plot_typeinsitu_log     0.16415    0.22352   0.734  0.46272   
## current_plot_typeinsitu_pvc     0.13402    0.21840   0.614  0.53944   
## current_plot_typeopen          -0.16911    0.21437  -0.789  0.43019   
## current_plot_typeopen_with_log -0.10599    0.24127  -0.439  0.66044   
## current_plot_typeopen_with_pvc  0.18007    0.20993   0.858  0.39102   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##                                 Estimate Std. Error z value Pr(>|z|)
## (Intercept)                      -5.2167     3.3577  -1.554    0.120
## namentrcy_tot                     1.6850     2.9719   0.567    0.571
## namentror_tot                     3.7206     3.0319   1.227    0.220
## current_plot_typeinsitu_log      -2.0663     2.4030  -0.860    0.390
## current_plot_typeinsitu_pvc      -0.7034     1.2255  -0.574    0.566
## current_plot_typeopen             1.2724     0.9896   1.286    0.199
## current_plot_typeopen_with_log    0.5711     1.0626   0.537    0.591
## current_plot_typeopen_with_pvc  -17.1386  3844.9771  -0.004    0.996
emmip(fit3,~current_plot_type|name, type='response',CI=T)

# visualize
est<-emmeans(fit3,~current_plot_type|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              0.849 0.190 Inf    1  -0.734  0.9777
##  gap / insitu_pvc              0.875 0.191 Inf    1  -0.614  0.9901
##  gap / open                    1.184 0.254 Inf    1   0.789  0.9695
##  gap / open_with_log           1.112 0.268 Inf    1   0.439  0.9979
##  gap / open_with_pvc           0.835 0.175 Inf    1  -0.858  0.9563
##  insitu_log / insitu_pvc       1.031 0.252 Inf    1   0.123  1.0000
##  insitu_log / open             1.396 0.341 Inf    1   1.362  0.7499
##  insitu_log / open_with_log    1.310 0.353 Inf    1   1.003  0.9172
##  insitu_log / open_with_pvc    0.984 0.231 Inf    1  -0.068  1.0000
##  insitu_pvc / open             1.354 0.323 Inf    1   1.271  0.8009
##  insitu_pvc / open_with_log    1.271 0.336 Inf    1   0.907  0.9448
##  insitu_pvc / open_with_pvc    0.955 0.220 Inf    1  -0.200  1.0000
##  open / open_with_log          0.939 0.244 Inf    1  -0.243  0.9999
##  open / open_with_pvc          0.705 0.164 Inf    1  -1.499  0.6650
##  open_with_log / open_with_pvc 0.751 0.195 Inf    1  -1.102  0.8809
## 
## name = ntrcy_tot:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              0.849 0.190 Inf    1  -0.734  0.9777
##  gap / insitu_pvc              0.875 0.191 Inf    1  -0.614  0.9901
##  gap / open                    1.184 0.254 Inf    1   0.789  0.9695
##  gap / open_with_log           1.112 0.268 Inf    1   0.439  0.9979
##  gap / open_with_pvc           0.835 0.175 Inf    1  -0.858  0.9563
##  insitu_log / insitu_pvc       1.031 0.252 Inf    1   0.123  1.0000
##  insitu_log / open             1.396 0.341 Inf    1   1.362  0.7499
##  insitu_log / open_with_log    1.310 0.353 Inf    1   1.003  0.9172
##  insitu_log / open_with_pvc    0.984 0.231 Inf    1  -0.068  1.0000
##  insitu_pvc / open             1.354 0.323 Inf    1   1.271  0.8009
##  insitu_pvc / open_with_log    1.271 0.336 Inf    1   0.907  0.9448
##  insitu_pvc / open_with_pvc    0.955 0.220 Inf    1  -0.200  1.0000
##  open / open_with_log          0.939 0.244 Inf    1  -0.243  0.9999
##  open / open_with_pvc          0.705 0.164 Inf    1  -1.499  0.6650
##  open_with_log / open_with_pvc 0.751 0.195 Inf    1  -1.102  0.8809
## 
## name = ntror_tot:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              0.849 0.190 Inf    1  -0.734  0.9777
##  gap / insitu_pvc              0.875 0.191 Inf    1  -0.614  0.9901
##  gap / open                    1.184 0.254 Inf    1   0.789  0.9695
##  gap / open_with_log           1.112 0.268 Inf    1   0.439  0.9979
##  gap / open_with_pvc           0.835 0.175 Inf    1  -0.858  0.9563
##  insitu_log / insitu_pvc       1.031 0.252 Inf    1   0.123  1.0000
##  insitu_log / open             1.396 0.341 Inf    1   1.362  0.7499
##  insitu_log / open_with_log    1.310 0.353 Inf    1   1.003  0.9172
##  insitu_log / open_with_pvc    0.984 0.231 Inf    1  -0.068  1.0000
##  insitu_pvc / open             1.354 0.323 Inf    1   1.271  0.8009
##  insitu_pvc / open_with_log    1.271 0.336 Inf    1   0.907  0.9448
##  insitu_pvc / open_with_pvc    0.955 0.220 Inf    1  -0.200  1.0000
##  open / open_with_log          0.939 0.244 Inf    1  -0.243  0.9999
##  open / open_with_pvc          0.705 0.164 Inf    1  -1.499  0.6650
##  open_with_log / open_with_pvc 0.751 0.195 Inf    1  -1.102  0.8809
## 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## Tests are performed on the log scale
#### now i'll do a by-hand hurdle model on my own using a truncated negative binomial

### zeros and ones 

dat22$presence<-ifelse(dat22$value==0, 0, 1)
zerofit<-glmmTMB(presence~name*current_plot_type+(1 | block), family=binomial, data=dat22, REML=FALSE)
emmip(zerofit,~current_plot_type|name, type='response',CI=T)

est<-emmeans(zerofit, ~current_plot_type|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast                      odds.ratio     SE  df null z.ratio p.value
##  gap / insitu_log                   2.226 1.5662 Inf    1   1.138  0.8656
##  gap / insitu_pvc                   0.886 0.6144 Inf    1  -0.174  1.0000
##  gap / open                         1.070 0.6176 Inf    1   0.118  1.0000
##  gap / open_with_log                1.374 0.9460 Inf    1   0.462  0.9974
##  gap / open_with_pvc                0.427 0.3307 Inf    1  -1.099  0.8821
##  insitu_log / insitu_pvc            0.398 0.3203 Inf    1  -1.145  0.8626
##  insitu_log / open                  0.481 0.3394 Inf    1  -1.037  0.9054
##  insitu_log / open_with_log         0.617 0.4935 Inf    1  -0.604  0.9908
##  insitu_log / open_with_pvc         0.192 0.1680 Inf    1  -1.886  0.4107
##  insitu_pvc / open                  1.208 0.8410 Inf    1   0.271  0.9998
##  insitu_pvc / open_with_log         1.551 1.2269 Inf    1   0.555  0.9938
##  insitu_pvc / open_with_pvc         0.482 0.4178 Inf    1  -0.842  0.9597
##  open / open_with_log               1.284 0.8874 Inf    1   0.362  0.9992
##  open / open_with_pvc               0.399 0.3101 Inf    1  -1.182  0.8456
##  open_with_log / open_with_pvc      0.311 0.2683 Inf    1  -1.354  0.7546
## 
## name = ntrcy_tot:
##  contrast                      odds.ratio     SE  df null z.ratio p.value
##  gap / insitu_log                   0.443 0.3956 Inf    1  -0.912  0.9437
##  gap / insitu_pvc                   0.367 0.3239 Inf    1  -1.136  0.8666
##  gap / open                         3.370 1.9788 Inf    1   2.069  0.3039
##  gap / open_with_log                0.669 0.5301 Inf    1  -0.507  0.9959
##  gap / open_with_pvc                0.894 0.6576 Inf    1  -0.153  1.0000
##  insitu_log / insitu_pvc            0.829 0.9107 Inf    1  -0.171  1.0000
##  insitu_log / open                  7.606 6.6977 Inf    1   2.304  0.1923
##  insitu_log / open_with_log         1.511 1.5526 Inf    1   0.402  0.9987
##  insitu_log / open_with_pvc         2.017 1.9871 Inf    1   0.712  0.9805
##  insitu_pvc / open                  9.178 7.9857 Inf    1   2.548  0.1105
##  insitu_pvc / open_with_log         1.823 1.8568 Inf    1   0.590  0.9917
##  insitu_pvc / open_with_pvc         2.434 2.3749 Inf    1   0.912  0.9436
##  open / open_with_log               0.199 0.1545 Inf    1  -2.079  0.2984
##  open / open_with_pvc               0.265 0.1911 Inf    1  -1.842  0.4385
##  open_with_log / open_with_pvc      1.335 1.1941 Inf    1   0.323  0.9995
## 
## name = ntror_tot:
##  contrast                      odds.ratio     SE  df null z.ratio p.value
##  gap / insitu_log                   0.581 0.4259 Inf    1  -0.740  0.9769
##  gap / insitu_pvc                   0.461 0.3304 Inf    1  -1.080  0.8895
##  gap / open                         1.734 0.9721 Inf    1   0.982  0.9239
##  gap / open_with_log                1.357 0.9183 Inf    1   0.451  0.9977
##  gap / open_with_pvc                0.188 0.1631 Inf    1  -1.927  0.3852
##  insitu_log / insitu_pvc            0.794 0.6831 Inf    1  -0.268  0.9998
##  insitu_log / open                  2.983 2.1972 Inf    1   1.484  0.6746
##  insitu_log / open_with_log         2.335 1.9333 Inf    1   1.024  0.9101
##  insitu_log / open_with_pvc         0.324 0.3201 Inf    1  -1.141  0.8644
##  insitu_pvc / open                  3.757 2.7057 Inf    1   1.838  0.4409
##  insitu_pvc / open_with_log         2.940 2.3920 Inf    1   1.326  0.7707
##  insitu_pvc / open_with_pvc         0.408 0.3980 Inf    1  -0.919  0.9418
##  open / open_with_log               0.783 0.5322 Inf    1  -0.360  0.9992
##  open / open_with_pvc               0.109 0.0945 Inf    1  -2.551  0.1097
##  open_with_log / open_with_pvc      0.139 0.1316 Inf    1  -2.082  0.2967
## 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## Tests are performed on the log odds ratio scale
# no significant differences

### abundance with a truncated negbinom 
dat22$posicounts<-as.numeric(ifelse(dat22$value==0, "NA", dat22$value))
countfit<-glmmTMB(posicounts~name*current_plot_type+(1 | block), family=truncated_nbinom2(), data=dat22, REML=FALSE)
emmip(countfit,~current_plot_type|name, type='response',CI=T)

est<-emmeans(countfit, ~current_plot_type|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              1.455 0.901 Inf    1   0.606  0.9906
##  gap / insitu_pvc              0.771 0.364 Inf    1  -0.551  0.9940
##  gap / open                    1.339 0.575 Inf    1   0.679  0.9843
##  gap / open_with_log           1.332 0.734 Inf    1   0.520  0.9954
##  gap / open_with_pvc           1.330 0.641 Inf    1   0.592  0.9916
##  insitu_log / insitu_pvc       0.530 0.351 Inf    1  -0.960  0.9305
##  insitu_log / open             0.920 0.582 Inf    1  -0.132  1.0000
##  insitu_log / open_with_log    0.915 0.658 Inf    1  -0.123  1.0000
##  insitu_log / open_with_pvc    0.914 0.610 Inf    1  -0.135  1.0000
##  insitu_pvc / open             1.737 0.853 Inf    1   1.124  0.8718
##  insitu_pvc / open_with_log    1.728 1.032 Inf    1   0.916  0.9425
##  insitu_pvc / open_with_pvc    1.726 0.919 Inf    1   1.024  0.9100
##  open / open_with_log          0.995 0.565 Inf    1  -0.009  1.0000
##  open / open_with_pvc          0.994 0.497 Inf    1  -0.013  1.0000
##  open_with_log / open_with_pvc 0.999 0.601 Inf    1  -0.002  1.0000
## 
## name = ntrcy_tot:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              0.655 0.298 Inf    1  -0.932  0.9383
##  gap / insitu_pvc              1.627 0.794 Inf    1   0.998  0.9188
##  gap / open                    2.575 1.419 Inf    1   1.717  0.5204
##  gap / open_with_log           1.189 0.579 Inf    1   0.355  0.9993
##  gap / open_with_pvc           0.889 0.420 Inf    1  -0.249  0.9999
##  insitu_log / insitu_pvc       2.485 1.322 Inf    1   1.712  0.5238
##  insitu_log / open             3.934 2.325 Inf    1   2.318  0.1868
##  insitu_log / open_with_log    1.815 0.966 Inf    1   1.121  0.8730
##  insitu_log / open_with_pvc    1.358 0.702 Inf    1   0.592  0.9916
##  insitu_pvc / open             1.583 0.972 Inf    1   0.748  0.9758
##  insitu_pvc / open_with_log    0.730 0.409 Inf    1  -0.561  0.9935
##  insitu_pvc / open_with_pvc    0.546 0.299 Inf    1  -1.103  0.8804
##  open / open_with_log          0.461 0.284 Inf    1  -1.256  0.8087
##  open / open_with_pvc          0.345 0.209 Inf    1  -1.755  0.4953
##  open_with_log / open_with_pvc 0.748 0.412 Inf    1  -0.528  0.9951
## 
## name = ntror_tot:
##  contrast                      ratio    SE  df null z.ratio p.value
##  gap / insitu_log              0.476 0.228 Inf    1  -1.552  0.6302
##  gap / insitu_pvc              0.745 0.347 Inf    1  -0.633  0.9886
##  gap / open                    0.604 0.272 Inf    1  -1.121  0.8730
##  gap / open_with_log           1.155 0.656 Inf    1   0.254  0.9999
##  gap / open_with_pvc           0.567 0.248 Inf    1  -1.296  0.7875
##  insitu_log / insitu_pvc       1.565 0.792 Inf    1   0.885  0.9502
##  insitu_log / open             1.270 0.625 Inf    1   0.486  0.9967
##  insitu_log / open_with_log    2.429 1.470 Inf    1   1.466  0.6858
##  insitu_log / open_with_pvc    1.193 0.574 Inf    1   0.366  0.9991
##  insitu_pvc / open             0.811 0.390 Inf    1  -0.435  0.9980
##  insitu_pvc / open_with_log    1.552 0.920 Inf    1   0.741  0.9768
##  insitu_pvc / open_with_pvc    0.762 0.357 Inf    1  -0.580  0.9923
##  open / open_with_log          1.913 1.108 Inf    1   1.120  0.8735
##  open / open_with_pvc          0.939 0.423 Inf    1  -0.139  1.0000
##  open_with_log / open_with_pvc 0.491 0.278 Inf    1  -1.256  0.8091
## 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## Tests are performed on the log scale
# no significant differences in abundance

############# treatment response: do analysis for total biomass by species #############


dat3<-(dat1[c(1:4,19,21,23,25)])
totwtdat<-as.data.frame(dat3 %>% pivot_longer(c(wt_max15_goro, wt_max15_tror, wt_max15_trcy)))
totwtdat$log_wt<-log1p(totwtdat$value) # log transformation does not help the insane amount of heteroscedasticity here, neither does a square root transformation.

# model
totwtmod<-lm(log_wt~name*current_plot_type, data=totwtdat) # fit is singular when including a random effect for block so not doing that

# test for fit
sim<-simulateResiduals(totwtmod) # insanely heteroscedastic, doesn't get better with sqrt transform (all transformations make it worse)
plot(sim)

# model summary - don't trust this the residual dispersion is fucked, commenting out.
# summary(totwtmod)
# emmip(totwtmod,~current_plot_type|name, CI=T)
# 
# est<-emmeans(totwtmod,~current_plot_type|name, type='response')
# pairs(est)

############# treatment response: do analysis for per capita biomass by species #############

# per capita might help with heteroscedasticity  
dat4<-(dat1[c(1:4,19,20,22,24)])
pcwtdat<-as.data.frame(dat4 %>% pivot_longer(c(wt_percapita_goro, wt_percapita_tror, wt_percapita_trcy)))
pcwtdat$log_wt<-log(pcwtdat$value) # log transform the weight data to get it normal looking - it is heteroscedastic otherwise

# model
pcwtmod<-lm(log_wt~name*current_plot_type, data=pcwtdat) # fit is singular with random effect.

# test for fit, looks pretty good
sim<-simulateResiduals(pcwtmod)
plot(sim) ## much better! 

# model summary
summary(pcwtmod)
## 
## Call:
## lm(formula = log_wt ~ name * current_plot_type, data = pcwtdat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.78438 -0.55882  0.03892  0.53364  2.07151 
## 
## Coefficients:
##                                                      Estimate Std. Error
## (Intercept)                                           -1.7528     0.2482
## namewt_percapita_trcy                                 -2.1342     0.3569
## namewt_percapita_tror                                 -1.2428     0.4754
## current_plot_typeinsitu_log                           -0.2453     0.4754
## current_plot_typeinsitu_pvc                            0.2613     0.4300
## current_plot_typeopen                                  0.2378     0.3511
## current_plot_typeopen_with_log                        -0.1498     0.4754
## current_plot_typeopen_with_pvc                        -0.2712     0.4003
## namewt_percapita_trcy:current_plot_typeinsitu_log     -0.1139     0.6753
## namewt_percapita_tror:current_plot_typeinsitu_log     -0.5584     0.7288
## namewt_percapita_trcy:current_plot_typeinsitu_pvc     -0.1540     0.5833
## namewt_percapita_tror:current_plot_typeinsitu_pvc     -1.4357     0.6874
## namewt_percapita_trcy:current_plot_typeopen           -0.1365     0.6214
## namewt_percapita_tror:current_plot_typeopen           -1.1933     0.7308
## namewt_percapita_trcy:current_plot_typeopen_with_log   0.7427     0.6442
## namewt_percapita_tror:current_plot_typeopen_with_log  -1.4992     0.8479
## namewt_percapita_trcy:current_plot_typeopen_with_pvc   0.7768     0.5909
## namewt_percapita_tror:current_plot_typeopen_with_pvc  -1.2434     0.6505
##                                                      t value Pr(>|t|)    
## (Intercept)                                           -7.061 7.92e-11 ***
## namewt_percapita_trcy                                 -5.980 1.88e-08 ***
## namewt_percapita_tror                                 -2.614  0.00995 ** 
## current_plot_typeinsitu_log                           -0.516  0.60664    
## current_plot_typeinsitu_pvc                            0.608  0.54437    
## current_plot_typeopen                                  0.677  0.49929    
## current_plot_typeopen_with_log                        -0.315  0.75309    
## current_plot_typeopen_with_pvc                        -0.677  0.49926    
## namewt_percapita_trcy:current_plot_typeinsitu_log     -0.169  0.86632    
## namewt_percapita_tror:current_plot_typeinsitu_log     -0.766  0.44488    
## namewt_percapita_trcy:current_plot_typeinsitu_pvc     -0.264  0.79211    
## namewt_percapita_tror:current_plot_typeinsitu_pvc     -2.089  0.03862 *  
## namewt_percapita_trcy:current_plot_typeopen           -0.220  0.82646    
## namewt_percapita_tror:current_plot_typeopen           -1.633  0.10484    
## namewt_percapita_trcy:current_plot_typeopen_with_log   1.153  0.25096    
## namewt_percapita_tror:current_plot_typeopen_with_log  -1.768  0.07930 .  
## namewt_percapita_trcy:current_plot_typeopen_with_pvc   1.315  0.19088    
## namewt_percapita_tror:current_plot_typeopen_with_pvc  -1.911  0.05808 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.993 on 135 degrees of freedom
##   (183 observations deleted due to missingness)
## Multiple R-squared:  0.5801, Adjusted R-squared:  0.5272 
## F-statistic: 10.97 on 17 and 135 DF,  p-value: < 2.2e-16
emmip(pcwtmod,~current_plot_type|name,CI=T)

est<-emmeans(totwtmod,~current_plot_type|name, type='response')
pairs(est)
## name = wt_max15_goro:
##  contrast                      estimate     SE  df t.ratio p.value
##  gap - insitu_log               0.06531 0.0967 136   0.675  0.9844
##  gap - insitu_pvc              -0.17108 0.0875 136  -1.955  0.3737
##  gap - open                    -0.05976 0.0714 136  -0.836  0.9602
##  gap - open_with_log            0.03726 0.0967 136   0.385  0.9989
##  gap - open_with_pvc           -0.00687 0.0815 136  -0.084  1.0000
##  insitu_log - insitu_pvc       -0.23640 0.1091 136  -2.166  0.2606
##  insitu_log - open             -0.12508 0.0967 136  -1.293  0.7885
##  insitu_log - open_with_log    -0.02805 0.1167 136  -0.240  0.9999
##  insitu_log - open_with_pvc    -0.07218 0.1044 136  -0.692  0.9826
##  insitu_pvc - open              0.11132 0.0875 136   1.272  0.7996
##  insitu_pvc - open_with_log     0.20835 0.1091 136   1.909  0.4012
##  insitu_pvc - open_with_pvc     0.16422 0.0959 136   1.713  0.5256
##  open - open_with_log           0.09703 0.0967 136   1.003  0.9163
##  open - open_with_pvc           0.05290 0.0815 136   0.649  0.9869
##  open_with_log - open_with_pvc -0.04413 0.1044 136  -0.423  0.9982
## 
## name = wt_max15_trcy:
##  contrast                      estimate     SE  df t.ratio p.value
##  gap - insitu_log               0.04197 0.0976 136   0.430  0.9981
##  gap - insitu_pvc               0.01608 0.0802 136   0.200  1.0000
##  gap - open                     0.03084 0.0976 136   0.316  0.9996
##  gap - open_with_log           -0.01903 0.0885 136  -0.215  0.9999
##  gap - open_with_pvc           -0.04116 0.0885 136  -0.465  0.9972
##  insitu_log - insitu_pvc       -0.02589 0.1026 136  -0.252  0.9999
##  insitu_log - open             -0.01113 0.1167 136  -0.095  1.0000
##  insitu_log - open_with_log    -0.06100 0.1091 136  -0.559  0.9934
##  insitu_log - open_with_pvc    -0.08313 0.1091 136  -0.762  0.9734
##  insitu_pvc - open              0.01476 0.1026 136   0.144  1.0000
##  insitu_pvc - open_with_log    -0.03511 0.0939 136  -0.374  0.9990
##  insitu_pvc - open_with_pvc    -0.05724 0.0939 136  -0.610  0.9902
##  open - open_with_log          -0.04987 0.1091 136  -0.457  0.9975
##  open - open_with_pvc          -0.07200 0.1091 136  -0.660  0.9859
##  open_with_log - open_with_pvc -0.02213 0.1010 136  -0.219  0.9999
## 
## name = wt_max15_tror:
##  contrast                      estimate     SE  df t.ratio p.value
##  gap - insitu_log               0.06360 0.1124 136   0.566  0.9930
##  gap - insitu_pvc               0.07855 0.1091 136   0.720  0.9793
##  gap - open                     0.05806 0.1304 136   0.445  0.9978
##  gap - open_with_log            0.08039 0.1429 136   0.563  0.9932
##  gap - open_with_pvc            0.06465 0.1044 136   0.620  0.9894
##  insitu_log - insitu_pvc        0.01495 0.1046 136   0.143  1.0000
##  insitu_log - open             -0.00554 0.1267 136  -0.044  1.0000
##  insitu_log - open_with_log     0.01679 0.1395 136   0.120  1.0000
##  insitu_log - open_with_pvc     0.00105 0.0996 136   0.011  1.0000
##  insitu_pvc - open             -0.02049 0.1238 136  -0.166  1.0000
##  insitu_pvc - open_with_log     0.00184 0.1368 136   0.013  1.0000
##  insitu_pvc - open_with_pvc    -0.01390 0.0959 136  -0.145  1.0000
##  open - open_with_log           0.02233 0.1543 136   0.145  1.0000
##  open - open_with_pvc           0.00659 0.1196 136   0.055  1.0000
##  open_with_log - open_with_pvc -0.01574 0.1330 136  -0.118  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
est2<-emmeans(totwtmod,~name, type='response')
pairs(est)
## name = wt_max15_goro:
##  contrast                      estimate     SE  df t.ratio p.value
##  gap - insitu_log               0.06531 0.0967 136   0.675  0.9844
##  gap - insitu_pvc              -0.17108 0.0875 136  -1.955  0.3737
##  gap - open                    -0.05976 0.0714 136  -0.836  0.9602
##  gap - open_with_log            0.03726 0.0967 136   0.385  0.9989
##  gap - open_with_pvc           -0.00687 0.0815 136  -0.084  1.0000
##  insitu_log - insitu_pvc       -0.23640 0.1091 136  -2.166  0.2606
##  insitu_log - open             -0.12508 0.0967 136  -1.293  0.7885
##  insitu_log - open_with_log    -0.02805 0.1167 136  -0.240  0.9999
##  insitu_log - open_with_pvc    -0.07218 0.1044 136  -0.692  0.9826
##  insitu_pvc - open              0.11132 0.0875 136   1.272  0.7996
##  insitu_pvc - open_with_log     0.20835 0.1091 136   1.909  0.4012
##  insitu_pvc - open_with_pvc     0.16422 0.0959 136   1.713  0.5256
##  open - open_with_log           0.09703 0.0967 136   1.003  0.9163
##  open - open_with_pvc           0.05290 0.0815 136   0.649  0.9869
##  open_with_log - open_with_pvc -0.04413 0.1044 136  -0.423  0.9982
## 
## name = wt_max15_trcy:
##  contrast                      estimate     SE  df t.ratio p.value
##  gap - insitu_log               0.04197 0.0976 136   0.430  0.9981
##  gap - insitu_pvc               0.01608 0.0802 136   0.200  1.0000
##  gap - open                     0.03084 0.0976 136   0.316  0.9996
##  gap - open_with_log           -0.01903 0.0885 136  -0.215  0.9999
##  gap - open_with_pvc           -0.04116 0.0885 136  -0.465  0.9972
##  insitu_log - insitu_pvc       -0.02589 0.1026 136  -0.252  0.9999
##  insitu_log - open             -0.01113 0.1167 136  -0.095  1.0000
##  insitu_log - open_with_log    -0.06100 0.1091 136  -0.559  0.9934
##  insitu_log - open_with_pvc    -0.08313 0.1091 136  -0.762  0.9734
##  insitu_pvc - open              0.01476 0.1026 136   0.144  1.0000
##  insitu_pvc - open_with_log    -0.03511 0.0939 136  -0.374  0.9990
##  insitu_pvc - open_with_pvc    -0.05724 0.0939 136  -0.610  0.9902
##  open - open_with_log          -0.04987 0.1091 136  -0.457  0.9975
##  open - open_with_pvc          -0.07200 0.1091 136  -0.660  0.9859
##  open_with_log - open_with_pvc -0.02213 0.1010 136  -0.219  0.9999
## 
## name = wt_max15_tror:
##  contrast                      estimate     SE  df t.ratio p.value
##  gap - insitu_log               0.06360 0.1124 136   0.566  0.9930
##  gap - insitu_pvc               0.07855 0.1091 136   0.720  0.9793
##  gap - open                     0.05806 0.1304 136   0.445  0.9978
##  gap - open_with_log            0.08039 0.1429 136   0.563  0.9932
##  gap - open_with_pvc            0.06465 0.1044 136   0.620  0.9894
##  insitu_log - insitu_pvc        0.01495 0.1046 136   0.143  1.0000
##  insitu_log - open             -0.00554 0.1267 136  -0.044  1.0000
##  insitu_log - open_with_log     0.01679 0.1395 136   0.120  1.0000
##  insitu_log - open_with_pvc     0.00105 0.0996 136   0.011  1.0000
##  insitu_pvc - open             -0.02049 0.1238 136  -0.166  1.0000
##  insitu_pvc - open_with_log     0.00184 0.1368 136   0.013  1.0000
##  insitu_pvc - open_with_pvc    -0.01390 0.0959 136  -0.145  1.0000
##  open - open_with_log           0.02233 0.1543 136   0.145  1.0000
##  open - open_with_pvc           0.00659 0.1196 136   0.055  1.0000
##  open_with_log - open_with_pvc -0.01574 0.1330 136  -0.118  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
# not any differences in per capita biomass among treatments within species, but goro is larger than the other two

#################################### PHYSICAL BARRIER ANALYSIS ####################################

######### What about the physical barrier - initial treatment for 2022 #######

# this is a model where do zero inflation and count together (occurrence and abundance)
fit_phys<-glmmTMB(value~name*physical_barrier+(1 | block), ziformula=~., family=nbinom2(), data=dat22, REML=F) #
sim<-simulateResiduals(fit_phys)
plot(sim)

testDispersion(sim) 

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.99867, p-value = 0.888
## alternative hypothesis: two.sided
testZeroInflation(sim)

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 0.95112, p-value = 0.664
## alternative hypothesis: two.sided
summary(fit_phys)
##  Family: nbinom2  ( log )
## Formula:          value ~ name * physical_barrier + (1 | block)
## Zero inflation:         ~.
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##   1229.9   1287.2   -600.0   1199.9      321 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.00587  0.07662 
## Number of obs: 336, groups:  block, 7
## 
## Zero-inflation model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 1.278    1.131   
## Number of obs: 336, groups:  block, 7
## 
## Dispersion parameter for nbinom2 family (): 1.59 
## 
## Conditional model:
##                                 Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                      0.51782    0.20178   2.566   0.0103 *
## namentrcy_tot                   -0.18481    0.27397  -0.675   0.4999  
## namentror_tot                    0.41609    0.26653   1.561   0.1185  
## physical_barrier1                0.04502    0.25651   0.176   0.8607  
## namentrcy_tot:physical_barrier1  0.33708    0.34860   0.967   0.3336  
## namentror_tot:physical_barrier1  0.15410    0.35400   0.435   0.6633  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##                                 Estimate Std. Error z value Pr(>|z|)
## (Intercept)                      -3.4280     2.8635  -1.197    0.231
## namentrcy_tot                     0.8232     2.8146   0.292    0.770
## namentror_tot                     2.6663     2.6805   0.995    0.320
## physical_barrier1                 0.7603     2.6214   0.290    0.772
## namentrcy_tot:physical_barrier1  -2.9115     4.4282  -0.657    0.511
## namentror_tot:physical_barrier1  -2.1069     2.7842  -0.757    0.449
emmip(fit_phys, ~physical_barrier|name, type='response', CI=T)

est<-emmeans(fit_phys,~physical_barrier|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.956 0.245 Inf    1  -0.176  0.8607
## 
## name = ntrcy_tot:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.682 0.168 Inf    1  -1.556  0.1198
## 
## name = ntror_tot:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.819 0.205 Inf    1  -0.795  0.4264
## 
## Tests are performed on the log scale
# no significant differences in total plants (occurrence and abundance) 

# split up occurrence and abundance
### zeros and ones 
zerofit_phys<-glmmTMB(presence~name*physical_barrier+(1 | block), family=binomial, data=dat22, REML=F)

sim<-simulateResiduals(zerofit_phys)
plot(sim)

testDispersion(sim) # looks good

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0146, p-value = 0.896
## alternative hypothesis: two.sided
testZeroInflation(sim) # looks good

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.0141, p-value = 0.96
## alternative hypothesis: two.sided
summary(zerofit_phys)
##  Family: binomial  ( logit )
## Formula:          presence ~ name * physical_barrier + (1 | block)
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##    430.3    457.0   -208.1    416.3      329 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.2193   0.4683  
## Number of obs: 336, groups:  block, 7
## 
## Conditional model:
##                                 Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                      0.58477    0.33785   1.731   0.0835 .
## namentrcy_tot                   -0.24130    0.40162  -0.601   0.5480  
## namentror_tot                   -0.55089    0.39934  -1.380   0.1677  
## physical_barrier1               -0.01595    0.40226  -0.040   0.9684  
## namentrcy_tot:physical_barrier1  1.17142    0.59819   1.958   0.0502 .
## namentror_tot:physical_barrier1  0.88272    0.57151   1.544   0.1225  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmip(zerofit_phys,~physical_barrier|name, type='response',CI=T)

est<-emmeans(zerofit_phys, ~physical_barrier|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      1.016 0.409 Inf    1   0.040
##  p.value
##   0.9684
## 
## name = ntrcy_tot:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.315 0.140 Inf    1  -2.608
##  p.value
##   0.0091
## 
## name = ntror_tot:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.420 0.171 Inf    1  -2.134
##  p.value
##   0.0329
## 
## Tests are performed on the log odds ratio scale
# physical barrier occurrence results
# physical barrier level does not significantly explain variation in goro 
# trcy and tror probability of occurrence are higher in places where there is a physical barrier (p=0.02 and p=0.009 respectively)

### abundance with a truncated negbinom 
countfit_phys<-glmmTMB(posicounts~name*physical_barrier+(1 | block), family=truncated_nbinom2(), data=dat22, REML=F)

sim<-simulateResiduals(countfit_phys)
plot(sim)

testDispersion(sim)

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.88162, p-value = 0.728
## alternative hypothesis: two.sided
testZeroInflation(sim)

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sided
summary(countfit_phys)
##  Family: truncated_nbinom2  ( log )
## Formula:          posicounts ~ name * physical_barrier + (1 | block)
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##    792.2    819.2   -388.1    776.2      209 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.003716 0.06096 
## Number of obs: 217, groups:  block, 7
## 
## Dispersion parameter for truncated_nbinom2 family (): 0.826 
## 
## Conditional model:
##                                  Estimate Std. Error z value Pr(>|z|)
## (Intercept)                      0.338919   0.279962   1.211    0.226
## namentrcy_tot                   -0.274546   0.339942  -0.808    0.419
## namentror_tot                    0.404083   0.331575   1.219    0.223
## physical_barrier1               -0.001728   0.326981  -0.005    0.996
## namentrcy_tot:physical_barrier1  0.270464   0.454932   0.595    0.552
## namentror_tot:physical_barrier1  0.214126   0.452559   0.473    0.636
emmip(countfit_phys,~physical_barrier|name, type='response',CI=T)

est<-emmeans(countfit_phys, ~physical_barrier|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.002 0.328 Inf    1   0.005  0.9958
## 
## name = ntrcy_tot:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.764 0.247 Inf    1  -0.833  0.4048
## 
## name = ntror_tot:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.809 0.251 Inf    1  -0.684  0.4937
## 
## Tests are performed on the log scale
# abundance does not differ between physical barrier treatments for any of the plant species


# model
# pcwtmod_phys<-lmer(log_wt~name*physical_barrier+(1|block), data=pcwtdat, REML=FALSE) # singular 
pcwtmod_phys<-lm(log_wt~name*physical_barrier, data=pcwtdat) 


# test for fit, looks pretty good
 sim<-simulateResiduals(pcwtmod_phys)
 plot(sim)

# model summary
summary(pcwtmod_phys)
## 
## Call:
## lm(formula = log_wt ~ name * physical_barrier, data = pcwtdat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.92995 -0.58816 -0.01781  0.55317  2.19042 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              -1.6339     0.1748  -9.345  < 2e-16
## namewt_percapita_trcy                    -2.2278     0.2819  -7.902 5.93e-13
## namewt_percapita_tror                    -1.7439     0.3583  -4.867 2.89e-06
## physical_barrier1                        -0.2187     0.2514  -0.870    0.386
## namewt_percapita_trcy:physical_barrier1   0.4301     0.3765   1.142    0.255
## namewt_percapita_tror:physical_barrier1  -0.6532     0.4426  -1.476    0.142
##                                            
## (Intercept)                             ***
## namewt_percapita_trcy                   ***
## namewt_percapita_tror                   ***
## physical_barrier1                          
## namewt_percapita_trcy:physical_barrier1    
## namewt_percapita_tror:physical_barrier1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9891 on 147 degrees of freedom
##   (183 observations deleted due to missingness)
## Multiple R-squared:  0.5463, Adjusted R-squared:  0.5309 
## F-statistic: 35.41 on 5 and 147 DF,  p-value: < 2.2e-16
emmip(pcwtmod_phys,~physical_barrier|name,CI=T)

est<-emmeans(pcwtmod_phys,~physical_barrier|name, type='response')
pairs(est)
## name = wt_percapita_goro:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1    0.219 0.251 147   0.870  0.3858
## 
## name = wt_percapita_trcy:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1   -0.211 0.280 147  -0.754  0.4518
## 
## name = wt_percapita_tror:
##  contrast                              estimate    SE  df t.ratio p.value
##  physical_barrier0 - physical_barrier1    0.872 0.364 147   2.393  0.0180
#################################### LEGACY ANALYSIS ####################################

############# log legacy response: do analysis for count by species ############# 
# fit3_leg<-glmmTMB(value~name*initial+(1 | block), ziformula=~., family=nbinom2(), data=dat22, REML=FALSE) # does not converge, i think it's poisson dist.
fit3_leg<-glmmTMB(value~name*initial+(1 | block), ziformula=~., family=poisson(), data=dat22, REML=FALSE) 

## This stuff will just give you the end result counts with the zeros factored in...
summary(fit3_leg)
##  Family: poisson  ( log )
## Formula:          value ~ name * initial + (1 | block)
## Zero inflation:         ~.
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##   1308.2   1361.7   -640.1   1280.2      322 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.01342  0.1158  
## Number of obs: 336, groups:  block, 7
## 
## Zero-inflation model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.3289   0.5735  
## Number of obs: 336, groups:  block, 7
## 
## Conditional model:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.93257    0.12586   7.410 1.27e-13 ***
## namentrcy_tot             -0.11740    0.16141  -0.727    0.467    
## namentror_tot              0.24085    0.15311   1.573    0.116    
## initialopen               -0.26979    0.17503  -1.541    0.123    
## namentrcy_tot:initialopen  0.01648    0.25354   0.065    0.948    
## namentror_tot:initialopen  0.35102    0.22307   1.574    0.116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##                           Estimate Std. Error z value Pr(>|z|)  
## (Intercept)               -0.71756    0.40605  -1.767   0.0772 .
## namentrcy_tot             -1.32361    0.68892  -1.921   0.0547 .
## namentror_tot             -0.03813    0.46329  -0.082   0.9344  
## initialopen               -0.55825    0.56794  -0.983   0.3256  
## namentrcy_tot:initialopen  1.49156    0.94728   1.575   0.1154  
## namentror_tot:initialopen  0.91272    0.71464   1.277   0.2015  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmip(fit3_leg,~initial|name, type='response',CI=T)

est<-emmeans(fit3_leg,~initial|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 1.310 0.229 Inf    1   1.541  0.1232
## 
## name = ntrcy_tot:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 1.288 0.237 Inf    1   1.380  0.1676
## 
## name = ntror_tot:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 0.922 0.127 Inf    1  -0.589  0.5561
## 
## Tests are performed on the log scale
#### split up occurrence and abundance
### zeros and ones 
zerofit_leg<-glmmTMB(presence~name*initial+(1 | block), family=binomial, data=dat22, REML=FALSE)
emmip(zerofit_leg,~initial|name, type='response',CI=T)

est<-emmeans(zerofit_leg, ~initial|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast   odds.ratio    SE  df null z.ratio p.value
##  log / open      0.785 0.316 Inf    1  -0.602  0.5473
## 
## name = ntrcy_tot:
##  contrast   odds.ratio    SE  df null z.ratio p.value
##  log / open      2.462 1.070 Inf    1   2.074  0.0381
## 
## name = ntror_tot:
##  contrast   odds.ratio    SE  df null z.ratio p.value
##  log / open      1.370 0.545 Inf    1   0.791  0.4287
## 
## Tests are performed on the log odds ratio scale
# probability of occurrence is lower for trcy in places where initial treatment is open (higher where there is a log legacy p=0.03)

### abundance with a truncated negbinom 
countfit_leg<-glmmTMB(posicounts~name*initial+(1 | block), family=truncated_nbinom2(), data=dat22, REML=FALSE)
emmip(countfit_leg,~initial|name, type='response',CI=T)

est<-emmeans(countfit_leg, ~initial|name, type='response')
pairs(est)
## name = ngoro_tot:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 1.397 0.449 Inf    1   1.041  0.2979
## 
## name = ntrcy_tot:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 1.334 0.423 Inf    1   0.908  0.3641
## 
## name = ntror_tot:
##  contrast   ratio    SE  df null z.ratio p.value
##  log / open 0.887 0.268 Inf    1  -0.397  0.6917
## 
## Tests are performed on the log scale
# model for per capita weight
# pcwtmod_leg<-lmer(log_wt~name*initial+(1|block), data=pcwtdat, REML=FALSE) #singular
pcwtmod_leg<-lm(log_wt~name*initial, data=pcwtdat)

# test for fit, looks pretty good
# sim<-simulateResiduals(pcwtmod_leg)
# plot(sim)

# model summary
summary(pcwtmod_leg)
## 
## Call:
## lm(formula = log_wt ~ name * initial, data = pcwtdat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0503 -0.5868 -0.0689  0.5899  2.3033 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       -1.73219    0.18030  -9.607  < 2e-16 ***
## namewt_percapita_trcy             -2.18533    0.25097  -8.708 5.88e-15 ***
## namewt_percapita_tror             -1.97868    0.28098  -7.042 6.75e-11 ***
## initialopen                       -0.01455    0.25097  -0.058   0.9539    
## namewt_percapita_trcy:initialopen  0.48763    0.37404   1.304   0.1944    
## namewt_percapita_tror:initialopen -0.67691    0.40841  -1.657   0.0996 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9876 on 147 degrees of freedom
##   (183 observations deleted due to missingness)
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5323 
## F-statistic:  35.6 on 5 and 147 DF,  p-value: < 2.2e-16
emmip(pcwtmod_leg,~initial|name,CI=T)

est<-emmeans(pcwtmod_leg,~initial|name, type='response')
pairs(est)
## name = wt_percapita_goro:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open   0.0145 0.251 147   0.058  0.9539
## 
## name = wt_percapita_trcy:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open  -0.4731 0.277 147  -1.706  0.0902
## 
## name = wt_percapita_tror:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open   0.6915 0.322 147   2.146  0.0335
# biomass per capita in tror is lower in open legacy environments (p=0.03); biomass per capita in trcy is higher in open legacy environments

#################################### PHYSICAL BARRIER X LEGACY ANALYSIS ####################################
######### What about both physical and legacy? ######### 

#### interaction model
### zeros and ones 
zerofit_intxn<-glmmTMB(presence~name*physical_barrier*initial+(1 | block), family=binomial, data=dat22, REML=F)

sim<-simulateResiduals(zerofit_intxn)
plot(sim)

testDispersion(sim)

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.0148, p-value = 0.816
## alternative hypothesis: two.sided
testZeroInflation(sim)

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = 1.0104, p-value = 0.928
## alternative hypothesis: two.sided
emmip(zerofit_intxn,initial~physical_barrier|name, type='response',CI=T)

est<-emmeans(zerofit_intxn, ~physical_barrier|name|initial, type='response')
pairs(est)
## name = ngoro_tot, initial = log:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      1.372 0.774 Inf    1   0.561
##  p.value
##   0.5745
## 
## name = ntrcy_tot, initial = log:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.403 0.280 Inf    1  -1.307
##  p.value
##   0.1911
## 
## name = ntror_tot, initial = log:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.516 0.300 Inf    1  -1.139
##  p.value
##   0.2549
## 
## name = ngoro_tot, initial = open:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.745 0.433 Inf    1  -0.507
##  p.value
##   0.6121
## 
## name = ntrcy_tot, initial = open:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.234 0.140 Inf    1  -2.433
##  p.value
##   0.0150
## 
## name = ntror_tot, initial = open:
##  contrast                              odds.ratio    SE  df null z.ratio
##  physical_barrier0 / physical_barrier1      0.336 0.193 Inf    1  -1.902
##  p.value
##   0.0571
## 
## Tests are performed on the log odds ratio scale
# goro presence/absence not explained by physical barrier or initial treatment 
# when the initial treatment is "open", trcy has higher prbability of occurring when there is a physical barrier. the same goes for tror.
# if there is a legacy of a log, then there is no significant difference between physical barrier treatments for tror or for trcy. 

### abundance with a truncated negbinom 
countfit_intxn<-glmmTMB(posicounts~name*physical_barrier*initial+(1 | block), family=truncated_nbinom2(), data=dat22, REML=F)

sim<-simulateResiduals(countfit_intxn)
plot(sim)

testDispersion(sim)

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 0.88343, p-value = 0.704
## alternative hypothesis: two.sided
testZeroInflation(sim)

## 
##  DHARMa zero-inflation test via comparison to expected zeros with
##  simulation under H0 = fitted model
## 
## data:  simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sided
emmip(countfit_intxn,~physical_barrier|name|initial, type='response',CI=T)

est<-emmeans(countfit_intxn, ~physical_barrier|name|initial, type='response')
pairs(est)
## name = ngoro_tot, initial = log:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.944 0.415 Inf    1  -0.130  0.8965
## 
## name = ntrcy_tot, initial = log:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.971 0.387 Inf    1  -0.075  0.9405
## 
## name = ntror_tot, initial = log:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.590 0.246 Inf    1  -1.268  0.2047
## 
## name = ngoro_tot, initial = open:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.022 0.465 Inf    1   0.047  0.9624
## 
## name = ntrcy_tot, initial = open:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 0.396 0.222 Inf    1  -1.655  0.0979
## 
## name = ntror_tot, initial = open:
##  contrast                              ratio    SE  df null z.ratio p.value
##  physical_barrier0 / physical_barrier1 1.131 0.496 Inf    1   0.281  0.7786
## 
## Tests are performed on the log scale
# abundance not explained by physical barrier or initial treatment for all three species

#### additive model 
zerofit_add<-glmmTMB(presence~name*physical_barrier+name*initial+(1 | block), family=binomial, data=dat22, REML=F)
summary(zerofit_add)
##  Family: binomial  ( logit )
## Formula:          
## presence ~ name * physical_barrier + name * initial + (1 | block)
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##    430.2    468.3   -205.1    410.2      326 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.2301   0.4797  
## Number of obs: 336, groups:  block, 7
## 
## Conditional model:
##                                 Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                      0.46829    0.39088   1.198   0.2309  
## namentrcy_tot                    0.38079    0.50971   0.747   0.4550  
## namentror_tot                   -0.26455    0.48701  -0.543   0.5870  
## physical_barrier1               -0.02049    0.40340  -0.051   0.9595  
## initialopen                      0.24376    0.40345   0.604   0.5457  
## namentrcy_tot:physical_barrier1  1.24985    0.60881   2.053   0.0401 *
## namentror_tot:physical_barrier1  0.90138    0.57391   1.571   0.1163  
## namentrcy_tot:initialopen       -1.23613    0.60711  -2.036   0.0417 *
## namentror_tot:initialopen       -0.58979    0.57314  -1.029   0.3035  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
countfit_add<-glmmTMB(posicounts~name*physical_barrier+name*initial+(1 | block), family=truncated_nbinom2(), data=dat22, REML=F)
summary(countfit_add)
##  Family: truncated_nbinom2  ( log )
## Formula:          posicounts ~ name * physical_barrier + name * initial + (1 |  
##     block)
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##    795.7    832.9   -386.8    773.7      206 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.001068 0.03268 
## Number of obs: 217, groups:  block, 7
## 
## Dispersion parameter for truncated_nbinom2 family (): 0.86 
## 
## Conditional model:
##                                 Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                      0.50851    0.30858   1.648   0.0994 .
## namentrcy_tot                   -0.32686    0.37921  -0.862   0.3887  
## namentror_tot                    0.18583    0.39190   0.474   0.6354  
## physical_barrier1                0.01149    0.32558   0.035   0.9718  
## initialopen                     -0.33362    0.31936  -1.045   0.2962  
## namentrcy_tot:physical_barrier1  0.32698    0.45821   0.714   0.4755  
## namentror_tot:physical_barrier1  0.20696    0.44973   0.460   0.6454  
## namentrcy_tot:initialopen       -0.02167    0.45416  -0.048   0.9619  
## namentror_tot:initialopen        0.46119    0.44224   1.043   0.2970  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### per capita biomass 

# model - intxn
# pcwtmod_intxn<-lmer(log_wt~name*physical_barrier*initial+(1|block), data=pcwtdat, REML=FALSE) # singular
pcwtmod_intxn<-lm(log_wt~name*physical_barrier*initial, data=pcwtdat)

# model - no intxn
# pcwtmod_add<-lmer(log_wt~name*physical_barrier+name*initial+(1|block), data=pcwtdat, REML=FALSE) # singular 
 pcwtmod_add<-lm(log_wt~name*physical_barrier+name*initial, data=pcwtdat) 
summary(pcwtmod_add)
## 
## Call:
## lm(formula = log_wt ~ name * physical_barrier + name * initial, 
##     data = pcwtdat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.93383 -0.49154 -0.02478  0.54954  2.19405 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             -1.630260   0.212724  -7.664 2.44e-12
## namewt_percapita_trcy                   -2.343605   0.312963  -7.488 6.41e-12
## namewt_percapita_tror                   -1.487665   0.396231  -3.755 0.000251
## physical_barrier1                       -0.218414   0.248409  -0.879 0.380728
## initialopen                             -0.007268   0.248409  -0.029 0.976699
## namewt_percapita_trcy:physical_barrier1  0.324482   0.377806   0.859 0.391846
## namewt_percapita_tror:physical_barrier1 -0.611710   0.437783  -1.397 0.164475
## namewt_percapita_trcy:initialopen        0.455888   0.375886   1.213 0.227179
## namewt_percapita_tror:initialopen       -0.642331   0.404507  -1.588 0.114494
##                                            
## (Intercept)                             ***
## namewt_percapita_trcy                   ***
## namewt_percapita_tror                   ***
## physical_barrier1                          
## initialopen                                
## namewt_percapita_trcy:physical_barrier1    
## namewt_percapita_tror:physical_barrier1    
## namewt_percapita_trcy:initialopen          
## namewt_percapita_tror:initialopen          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9769 on 144 degrees of freedom
##   (183 observations deleted due to missingness)
## Multiple R-squared:  0.5664, Adjusted R-squared:  0.5423 
## F-statistic: 23.51 on 8 and 144 DF,  p-value: < 2.2e-16
emmip(pcwtmod_add,~physical_barrier|name|initial, type='response',CI=T)

est<-emmeans(pcwtmod_add, ~initial|name, type='response')
pairs(est)
## name = wt_percapita_goro:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open  0.00727 0.248 144   0.029  0.9767
## 
## name = wt_percapita_trcy:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open -0.44862 0.282 144  -1.590  0.1140
## 
## name = wt_percapita_tror:
##  contrast   estimate    SE  df t.ratio p.value
##  log - open  0.64960 0.319 144   2.035  0.0437
## 
## Results are averaged over the levels of: physical_barrier
##### 2022 candidate model comparison ##### 

## zeros 
zero_candmods<-list("Plot type"=zerofit, 
                    "Physical barrier"=zerofit_phys,
                    "Nutrient island"=zerofit_leg,
                    "Physical Barrier + Nutrient Island"=zerofit_add,
                    "Physical Barrier x Nutrient Island"=zerofit_intxn)
aictab(zero_candmods)
## 
## Model selection based on AICc:
## 
##                                     K   AICc Delta_AICc AICcWt Cum.Wt      LL
## Physical barrier                    7 430.60       0.00   0.50   0.50 -208.13
## Physical Barrier + Nutrient Island 10 430.84       0.24   0.44   0.94 -205.08
## Physical Barrier x Nutrient Island 13 436.10       5.49   0.03   0.98 -204.48
## Nutrient island                     7 436.99       6.39   0.02   1.00 -211.33
## Plot type                          19 440.88      10.28   0.00   1.00 -200.24
# best fit model is a tie between physical barrier and physical barrier + nutrient island
# i will show the results for physical barrier + nutrient island. 

# counts
count_candmods<-list("Plot type"=countfit, 
                     "Physical barrier"=countfit_phys,
                     "Nutrient island"=countfit_leg,
                     "Physical Barrier + Nutrient Island"=countfit_add,
                     "Physical Barrier x Nutrient Island"=countfit_intxn)
aictab(count_candmods)
## 
## Model selection based on AICc:
## 
##                                     K   AICc Delta_AICc AICcWt Cum.Wt      LL
## Nutrient island                     8 791.95       0.00   0.58   0.58 -387.63
## Physical barrier                    8 792.85       0.89   0.37   0.95 -388.08
## Physical Barrier + Nutrient Island 11 796.98       5.02   0.05   0.99 -386.84
## Physical Barrier x Nutrient Island 14 800.90       8.94   0.01   1.00 -385.41
## Plot type                          20 808.82      16.86   0.00   1.00 -382.26
# best fit model is nutrient island, tied with physical barrier. 
# i will show the results for nutrient island.

pcwt_candmods<-list("Plot type"=pcwtmod, 
                    "Physical barrier"=pcwtmod_phys,
                    "Nutrient island"=pcwtmod_leg,
                    "Physical Barrier + Nutrient Island" = pcwtmod_add,
                    "Physical Barrier x Nutrient Island"=pcwtmod_intxn)
aictab(pcwt_candmods)
## 
## Model selection based on AICc:
## 
##                                     K   AICc Delta_AICc AICcWt Cum.Wt      LL
## Nutrient island                     7 439.02       0.00   0.37   0.37 -212.12
## Physical Barrier + Nutrient Island 10 439.33       0.31   0.31   0.68 -208.89
## Physical barrier                    7 439.48       0.46   0.29   0.97 -212.35
## Physical Barrier x Nutrient Island 13 444.20       5.19   0.03   1.00 -207.79
## Plot type                          19 456.61      17.59   0.00   1.00 -206.45
# best fit model is nutrient island, tied with physical barrier + nutrient island, and then physical barrier.
# i will show the results for the additive model. 

# tror is bigger when the legacy of the log and the log is still present

### figures ###
# best fit models - zeros 
# colors
mimiscols<-c("#D66972","#108780")
zerofit_add<-glmmTMB(presence~name*physical_barrier+name*initial+(1 | block), family=binomial, data=dat22, REML=FALSE)

zfit_est<-as.data.frame(emmeans(zerofit_add,~initial|physical_barrier|name, type='response'))

pl4<-ggplot(zfit_est,aes(physical_barrier,prob,group=initial),)+
  scale_color_manual(values=mimiscols)+
  geom_point(aes(col=initial), size=2, position=position_dodge(width=0.5))+
  geom_linerange(aes(ymin=asymp.LCL, ymax=asymp.UCL, col=initial), position=position_dodge(width=0.5))+
  geom_line(aes(col=initial), position=position_dodge(width=0.5))+
  facet_wrap(vars(name))+
  theme_bw()+
  theme(strip.text.x = element_text(size=0),
        strip.background = element_blank(),
        axis.text=element_text(size=15),
        axis.title=element_text(size=20),
        legend.text=element_text(size=15),
        legend.title=element_text(size=15),
        legend.position="top")+
  xlab("Physical Barrier")+
  ylab("Occurrence")+
  geom_jitter(data=dat22,
              aes(x=physical_barrier, y=presence, color=initial), 
              height=0.1,
              alpha=0.5)+
  labs(color = "Initial Plot Type")
# pl4
summary(zerofit_add)
##  Family: binomial  ( logit )
## Formula:          
## presence ~ name * physical_barrier + name * initial + (1 | block)
## Data: dat22
## 
##      AIC      BIC   logLik deviance df.resid 
##    430.2    468.3   -205.1    410.2      326 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.2301   0.4797  
## Number of obs: 336, groups:  block, 7
## 
## Conditional model:
##                                 Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                      0.46829    0.39088   1.198   0.2309  
## namentrcy_tot                    0.38079    0.50971   0.747   0.4550  
## namentror_tot                   -0.26455    0.48701  -0.543   0.5870  
## physical_barrier1               -0.02049    0.40340  -0.051   0.9595  
## initialopen                      0.24376    0.40345   0.604   0.5457  
## namentrcy_tot:physical_barrier1  1.24985    0.60881   2.053   0.0401 *
## namentror_tot:physical_barrier1  0.90138    0.57391   1.571   0.1163  
## namentrcy_tot:initialopen       -1.23613    0.60711  -2.036   0.0417 *
## namentror_tot:initialopen       -0.58979    0.57314  -1.029   0.3035  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pairs(emmeans(zerofit_add, ~physical_barrier|name|initial))
## name = ngoro_tot, initial = log:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   0.0205 0.403 Inf   0.051  0.9595
## 
## name = ntrcy_tot, initial = log:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1  -1.2294 0.456 Inf  -2.694  0.0071
## 
## name = ntror_tot, initial = log:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1  -0.8809 0.408 Inf  -2.156  0.0310
## 
## name = ngoro_tot, initial = open:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1   0.0205 0.403 Inf   0.051  0.9595
## 
## name = ntrcy_tot, initial = open:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1  -1.2294 0.456 Inf  -2.694  0.0071
## 
## name = ntror_tot, initial = open:
##  contrast                              estimate    SE  df z.ratio p.value
##  physical_barrier0 - physical_barrier1  -0.8809 0.408 Inf  -2.156  0.0310
## 
## Results are given on the log odds ratio (not the response) scale.
# tror and trcy do better where there is a physical barrier as compared to when there is not (tror p=0.03, trcy p=0.007)

pairs(emmeans(zerofit_add, ~initial|name|physical_barrier))
## name = ngoro_tot, physical_barrier = 0:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open   -0.244 0.403 Inf  -0.604  0.5457
## 
## name = ntrcy_tot, physical_barrier = 0:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open    0.992 0.453 Inf   2.189  0.0286
## 
## name = ntror_tot, physical_barrier = 0:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open    0.346 0.407 Inf   0.850  0.3951
## 
## name = ngoro_tot, physical_barrier = 1:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open   -0.244 0.403 Inf  -0.604  0.5457
## 
## name = ntrcy_tot, physical_barrier = 1:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open    0.992 0.453 Inf   2.189  0.0286
## 
## name = ntror_tot, physical_barrier = 1:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open    0.346 0.407 Inf   0.850  0.3951
## 
## Results are given on the log odds ratio (not the response) scale.
# trcy does better with log initial as compared to open initial (p=0.03)

 countfit_leg<-glmmTMB(posicounts~name*initial+(1 | block), family=truncated_nbinom2(), data=dat22, REML=FALSE)

cfit_est<-as.data.frame(emmeans(countfit_leg,~initial|name, type='response'))

pl5<-ggplot(cfit_est,aes(initial, response, group=1))+
  geom_jitter(data=dat22,
              aes(x=initial, y=posicounts),
              width=0.1,
              alpha=0.4,
              color="gray")+
  geom_point(size=2, color="black")+
  geom_linerange(aes(ymin=asymp.LCL, ymax=asymp.UCL),color="black")+
  geom_line(color="black")+
  facet_wrap(vars(name))+
  theme_bw()+
  theme(strip.text.x = element_text(size=0),
        strip.background = element_blank(),
        axis.text=element_text(size=15),
        axis.title=element_text(size=20),
        legend.text=element_text(size=15),
        legend.title=element_text(size=15))+
  xlab("Initial Plot Type")+
  ylab("Abundance")+
  labs(color = "Initial Plot \n Type")
pairs(emmeans(countfit_leg, ~initial|name))
## name = ngoro_tot:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open    0.334 0.321 Inf   1.041  0.2979
## 
## name = ntrcy_tot:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open    0.288 0.317 Inf   0.908  0.3641
## 
## name = ntror_tot:
##  contrast   estimate    SE  df z.ratio p.value
##  log - open   -0.120 0.303 Inf  -0.397  0.6917
## 
## Results are given on the log (not the response) scale.
# pl5

pcbfit_est<-as.data.frame(emmeans(pcwtmod_add,~physical_barrier|name|initial, type='response'))


pl6<-ggplot(pcbfit_est,aes(physical_barrier, emmean, group=initial))+
  scale_color_manual(values=mimiscols)+
  geom_point(aes(col=initial), size=2, position=position_dodge(width=0.5))+
  geom_linerange(aes(ymin=lower.CL, ymax=upper.CL, col=initial), position=position_dodge(width=0.5))+
  geom_line(aes(col=initial), position=position_dodge(width=0.5))+
  facet_wrap(vars(name))+
  theme_bw()+
  theme(strip.text.x = element_text(size=0),
        strip.background = element_blank(),
        axis.text=element_text(size=15),
        axis.title=element_text(size=20),
        legend.text=element_text(size=15),
        legend.title=element_text(size=15),
        legend.position="top")+
  xlab("Physical Barrier")+
  ylab("log(Biomass)")+
  geom_jitter(data=pcwtdat,
              aes(x=physical_barrier, y=log_wt, color=initial), 
              height=0.1,
              alpha=0.5)+
  labs(color = "Initial Plot Type")
#pl6
ggarrange(pl4, pl5, pl6, ncol=1, common.legend = T)